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The Girlflesh Castle(2008)
(The second book in the Girlflesh series)
A novel by Adriana Arden
Vanessa Buckingham has discovered strange contentment in the bizarre and secretive underworld of commercially organized slavery. Having accepted her own submissive nature, Vanessa is now happily working for the powerful Shiller Company as a "slave reporter" for its select publication Girlflesh News. She has also found a lover in the form of the beautiful slavegirl, Kashika. But there are forces at work that wish to destroy Shiller's carefully run unethical, slave business. Shiller's rival and archenemy the media mogul, Sir Harvey Rochester, has not given up trying to take over its operation. Having failed to use Vanessa as his unwitting pawn to expose Shiller's operation, Sir Harvey turns to other methods.
Similar Books by other authors...
Paperback Editions
December 2008 : UK Mass Market Paperback
|Title: The Girlflesh Castle (Nexus)
Author(s): Adriana Arden
ISBN: 0-352-34504-7 / 978-0-352-34504-2 (UK edition)
Publisher: Nexus
Availability: Amazon Amazon UK Amazon CA
Kindle Editions
December 2008 : Canada, UK Kindle edition
|Title: The Girlflesh Castle (Nexus)
Author(s): Adriana Arden
Publisher: Virgin Digital
Availability: Amazon UK Amazon CA


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Your journey starts here
Greencore Group is a leading international manufacturer of convenience foods. We have a passion for great food, world class business processes and talented people. To find out more about what it's like to work at Greencore click here.
To see our current vacancies, use our job search form.Sign up for job alerts
We employ talented people with an array of different skills and expertise to enable us to grow as a business and innovate in the food sector. Current vacancies can be found on our jobs pages. Here are some of the roles we recruit for.
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It has come to our attention that jobs at Greencore are being advertised without our permission on websites or job boards of employment agencies who we do not work with. Please be aware that Greencore uses a limited number of recruitment agencies. These are currently only in the UK , Ireland and the US. Please note that Greencore will not progress any job applications which are not commenced either direct or through our partner recruitment agencies.

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883 S. Curtis Ave., Pea Ridge, AR 72751
- $229,900
- 315 Days on site
- Est: $852/month
Estimate Your Monthly Payment
Data provided by Informa Research Services. Payments do not include amounts for taxes and insurance premiums. The actual payment obligation will be greater if taxes and insurance are included. Click here for more information on rates and product details.
- Mortgage Center
- Beds: 3
- Baths: 2 Full, 1 Half
- Single Family Home
- 2,781 sq ft
BEAUTIFULLY LANDSCAPED & REMODELED! Fantastic 3 Bd/2. 5 Bth ranch style home sits on a large corner lot in beautiful Pea Ridge and is completely redone and ready to buy now! Wood tile floors, new counters & backsplash in kitchen with new fixtures, large utility room, the possibilities are endless! Sit out on the deck and enjoy the large backyard & sometimes see some cows. Did you see the detached garage/workshop? Circular drive and mature trees! This house will not last long! Click here to learn more about this listing.
- Year Built: 1980
- 315 Days on site
- Single Family Home
- Beds: 3
- Baths: 2 Full, 1 Half
Listing Details
- Appliances
- Dishwasher, Disposal, Icemaker, Range, Range Hood
- Flooring
- Ceramic, Vinyl, Wood
- Architecture
- Ranch
- Lot Size
- Approximately 30492 Square Feet
- Roof
- Asphalt Roof
- Features
- Brick Exterior
- Cooling
- Ceiling Fan, Central Air, Central Cooling
- Heating
- Gas
- Assessed Taxable Amount
- $25,510
- Tax Amount
- $1,446
- Property Type
- Single Family Residence
- Bathrooms
- 2
- Floors
- 1
- Home Size (Sqft)
- 2,781
- Lot Size (Sqft)
- 14,374
- Lot Size (Acres)
- 0.33
- Units
- 1
- Fireplace
- Yes
- Parking
- Yes
- Heating System
- Yes
- Cooling System
- Central Air
- Exterior Type
- Brick/Stone
- Foundation Type
- Pier
- Roof Type
- Asphalt
Public Facts: As provided by County Assessor and Recorder offices. May not reflect recent additions or improvements. If you see any errors, the best way to get the records updated is to contact your local county records office. If any of the information in this section conflicts with property information provided by a listing agent or broker, agent-provided information should be deemed more reliable.
Local Info
| Median Household Income
| Median Years In Residence
| Median Homeowner Age
| Households with Children
| Percent of Homes Rented
Nearby Elementary Schools
- Pea Ridge Primary School
- 1411 Weston St
- Pea Ridge Intermediate School
- 1536 N Davis St
- Walnut Farm Montessori School
- 4208 E Central Ave
Nearby Middle Schools
- Pea Ridge Middle School
- 1391 Weston St
- Washington Junior High School
- 1501 Ne Wildcat Way
- Lincoln Junior High School
- 1206 Leopard Ln
Nearby High Schools
- Pea Ridge High School
- 781 W Pickens Rd
- Benton Cty School Of Arts High School
- 506 W Poplar St
- Bentonville High School
- 1901 Se J St
These are the closest schools to your requested location. Proximity does not guarantee enrollment eligibility. Please consult your real estate agent or school district to confirm the schools you are zoned to attend.
Mortgage Rates
Data provided by Informa Research Services. Payments do not include amounts for taxes and insurance premiums. The actual payment obligation will be greater if taxes and insurance are included. Click here for more information on rates and product details.
Property Contact Info
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Fish River Lodge
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Nomad Day 3 - Aye yi yi in Ai Ais Lodge
... returns to shore and an interesting chat with a South African who is quite the outdoorsman and amateur wildlife photographer, we eat a nice lunch and head out.
The vast expanse of the desert is soon revealed. On the east side of the truck sandy/rocky terrain with large hills of sand and rock prevails, while on the west side of the truck the landscape is composed of more scrub bush, with some green bordering the Orange River. As an aside, ...
Second biggest hole in the ground Apparently.
Well an early morning walk around Canyon Lodge certainly brought out the beauty of this place.
Set among towering rocks one would not know this place existed. The chalets are surrounded by lush green grass - such a contrast to the surrounds- and much to the delight of the local Jack Rabbits, Kudu, and Springbok. The Oryx are not welcome as they yank ...
Halloween Cruising the Chobe River
... hot when we left. It felt great to be on the water cruising down the river. Our bait were called bulldog fish. They were pretty good size, maybe five to seven inches. They put three huge hooks in each bait. We were using twelve pound line. I only had one big bite. You have to let them swallow the bait before you pull and reel. I could not wait. As soon as I felt the strike, I yanked and lost him. It was still lots of fun and very enjoyable. Tomorrow, more of the ...
Laura, Hilderbeest and I tour Namibia!
... and say thanks! So after an evening of discussing our trip we were very eager to get on the road the next morning. We went to the car rental place to pick up our vehicle bright and early and were so excited to see the 4WD we had hired! It was very bad ass. Just like us. The guy gave us a demo of the roof top tent and we packed the camping gear and we were off!
For our first day we had a 500km odd drive to reach Etosha NP. This was our only day of sealed road driving so it was ...
Cage diving with great whites!
... stopped everyone went up the top, the guy threw out some bait and within 10 minutes a shark appeared out of nowhere and started swimming round the boat. The cage was lowered in and people changed into wetsuits (not fun when cold and feeling slightly seasick, although nothing compared to the woman next to me who was throwing up so much she never got her wetsuit on!) And then people started getting in the cage group by group. The water was freezing, but once they shouted 'down' and you ...


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Old Lyme - The Zoning Commission unanimously voted Monday to uphold a town regulation that prohibits beer sales near a house of worship.
Local zoning regulations ban all alcohol sales within 200 feet of a religious institution. But a convenience store owner near Sound View Beach had sought to ease the restriction, so stores like his located near churches could sell beer only and no other types of alcohol - as long as they have a permit.
Citing the public's opinion on the matter, the commission denied Asif Choudhry's request on Monday. Several residents objected to changing the requirement at a public hearing continued from last month.
Some residents took issue with making alcohol more available in town, while two business owners, including Choudhry, said changing the regulation would be good for business.
Resident Glenn Bair said he had concerns as a parent with increasing access to alcohol. He was also worried about the potential for drunken-driving accidents near the beach.
At the opening of the public hearing last month, several residents had also said revising the law could raise the number of liquor establishments in the town, which they said could lead to public health and safety issues.
On Monday, Choudhry, the owner of the Best Way convenience store and gas station at 281 Shore Road, said he has spent a lot of resources to maintain his business for more than a decade. The store stands across the street from The Shoreline Church at 287 Shore Road.
"I did everything possible to sustain the business," said Choudhry.
He said he had listed his business for sale, but wanted to seek a beer license to increase his offerings and stay in business.
He spoke about the prevalence of beer elsewhere and also said that he wouldn't be able to sell to underage children by law.
Saying he was speaking up for all the businesses in Sound View, he spoke about the challenges of running a business in the seasonal area.
He also mentioned that nobody from the church opposed the amendment.
Clinton Brown of DiCesare-Bentley Engineers, speaking on behalf of Choudhry, also handed a petition to the commission in support of the amendment change with 80 signatures.
In their vote, commission members said they had heard the public's opinion — more so than usual — on the issue. Their reasons also included preserving the rural character of the town — an objective of the town's zoning regulations — and finding no compelling reason to change the regulation.
Commissioner John Johnson also echoed some of the residents' concerns in his comments.
"I don't think we need to make alcohol any more available to kids than it already is," he said.
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Trump calls trip 'home run' as Kushner questions left unanswered


text: Massachusetts Gaming Commission Massachusetts Gaming Commission Steps Into Boston-Wynn Dispute By Paul Tuthill • Jul 15, 2014 Massgaming.com Massachusetts gambling industry regulators said today they will be prepared to impose the terms of mitigation payments to the city of Boston from the developer of a proposed casino in a neighboring community. Boston is boycotting arbitration proceedings with Wynn Resorts – one of two competitors for the lone casino license in eastern Massachusetts Gambling Commission Won't Wait On Referendum For Licensing Decision By Paul Tuthill • Jul 2, 2014 The Massachusetts Gaming Commission will not delay a decision on awarding the state's most lucrative casino license until after the state's voters weigh in on the fate of casino gambling. It means the locations of proposed casinos will likely be settled by the time voters go to the polls in November. Regulators Approve MGM Springfield Casino By Paul Tuthill • Jun 13, 2014 WAMC The Massachusetts Gaming Commission has assigned the state's first resort casino license to MGM. The entertainment industry giant got the go-ahead from the industry regulators today to build an $800 million casino in downtown Springfield, but a cloud of uncertainty still hangs over the project. The unanimous vote by the five-member gaming commission was greeted with a standing ovation from more than 300 people who packed a ballroom inside the MassMutual Convention Center less than a block from where the casino would be built. Mass. Gaming Commissioners Report Positive Evaluations Of MGM Springfield Casino By Paul Tuthill • Jun 10, 2014 MGM Springfield Massachusetts gaming industry regulators began a final review today of MGM's proposed casino in Springfield. It is expected to conclude Friday with a decision to award the state's first resort casino license. Massachusetts Gaming Commissioners gave marks of "sufficient" to "outstanding" as they reported publicly on their evaluation of MGM's application for a gaming license to build an $800 million casino in downtown Springfield. Massachusetts Gaming Regulators Issue Revised Licensing Schedule By Paul Tuthill • May 21, 2014 Massgaming.com Gaming industry regulators in Massachusetts have announced a revised timetable for issuing resort casino licenses. Delays are costing the state and municipal governments gaming revenue that budget writers had counted on receiving by now. Massachusetts Gaming Commission Chairman Says He Will Not Step Down By Paul Tuthill • May 15, 2014 The Massachusetts Gaming Commission is on track to make a decision on June 13th to award the lone resort casino license in western Massachusetts. The commission chairman says he will be part of the decision- making process. Chairman Stephen Crosby presided over the commission's public hearing in Springfield Wednesday, the first public meeting for the gaming regulators since Crosby removed himself last week from any further role in the casino licensing process in the greater Boston area. Massachusetts Gaming Commission To Hold Final Public Hearing On MGM Springfield Casino By Paul Tuthill • May 14, 2014 WAMC The Massachusetts Gaming Commission is scheduled to hold a final public hearing in Springfield today on the casino proposed by MGM. It is a last chance for commissioners to gauge public sentiment before completing a lengthy evaluation of the sole resort casino applicant in western Massachusetts. Impartiality Questioned Of Some Players In Massachusetts Casino Scene By Paul Tuthill • May 12, 2014 Questions about ethics and possible conflicts of interest are clouding the effort to bring casino gambling to Massachusetts. There are calls for the chairman of the Massachusetts Gaming Commission to step down, and questions about the impartiality of a justice who will help decide if voters get a say on the fate of casinos. Arbitration Awards For Casino Impacts Upheld By Paul Tuthill • May 5, 2014 MGM Springfield Two western Massachusetts communities that went through an arbitration process to obtain payments intended to offset the impacts of a casino in Springfield appear to have done better financially than municipalities that accepted offers from casino giant MGM Resorts. Longmeadow and West Springfield went to arbitration with MGM after failing to reach negotiated settlements with the casino company by an end-of- March deadline established by the Massachusetts Gaming Commission. By then MGM had reached surrounding community agreements with six other municipalities. Anti-Casino Activist Says MGM's License Delay Request Is Evidence Of Eroding Public Support By Paul Tuthill • Apr 18, 2014 After a two-year battle that left MGM Resorts International as the last competitor standing to build a resort casino in western Massachusetts, the entertainment giant now wants to delay the ultimate prize—a Massachusetts casino license. A leading anti-casino advocate says it's a sign MGM is nervous about the prospect Massachusetts voters could repeal casino gambling in a statewide referendum. Pages« first ‹ previous 1 2 3 4 5 6 7 next › last »


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Frederic Malle
Musc Ravageur 10ml
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Editor's notes
A focal point in Amber Oriental history the Musc Ravageur originally launched in 2000 is a powerful yet perfectly controlled perfume. An explosive departure of bergamot, tangerine and cinnamon is set against a backdrop of vanilla, musk and amber. Turbulent and emphatic, in a word: ravageur.
Product features
- Creator: Mauric Roucel
- 10ml
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We offer a range of UK delivery options including standard delivery, next day delivery and various weekend options. The available delivery options and prices will vary depending on the delivery location you share with us when checking out.
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We deliver to a number of International countries, our full list of countries is available during checkout. You can change your delivery country at any time using the country selector located in the footer of our site.
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Our Click & Collect Service is quick and simple. Choose collect my items from one UK location during checkout and we'll email you when your order has been processed, it will be ready for collection within 3 working days.
If you are not completely satisfied with your item you may return it within 28 days for a full refund. You can return the item online using our simple service or bring it back to our flagship store in Regent St.


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zitten en liggen, makkelijk en betaalbaar wonen! een mooie meubelwinkel te gouda. ons assortiment bestaat uit landelijke en trendy meubels, riviera maison stijl, goedkope kasten, mandenkasten, buffetkasten, winkelkasten, boekenkasten, kledingkasten, dressoirs, tafels, eettafels, salontafels, sidetables, stoelen, barkrukken, feauteils, banken, bankstellen, bedden, boxsprings, matrassen, dekbedden, woonsets en accessoires.
Google Analytics is a service offered by Google that generates detailed statistics about a website's traffic and traffic sources and measures conversions and sales. Google Analytics can track visitors from all referrers, including search engines and social networks, direct visits and referring sites. It also displays advertisin...
StatCounter, a free online visitor stats tool. It offers its members the chance to grow and improve their online businesses by allowing them to monitor the number of hits to their website; the geographical location of visitors; the various pages a visitor views; keywords used to find the site plus other features.
Websites using some type of Facebook technology.
See what your Facebook friends have liked, shared, or commented on across the Web. This includes all variations of Like, Share and Follow Buttons, Embedded Posts, Comments, Activity Feed, Recommendations Feed, Recommendations Bar, Like Box and Facepile.
Facebook's Like button is the quickest way for people to share content with their friends. A single click on the Like button will 'like' pieces of content on the web and share them on Facebook. You can also display a Share button next to the Like button to let people add a personal message and customize who they share with.
This site uses the viewport meta tag which means the content may be optimized for mobile content.
The Apache HTTP Server is an open-source HTTP server for modern operating systems including UNIX, Microsoft Windows, Mac OS/X and Netware. The goal of this project is to provide a secure, efficient and extensible server that provides HTTP services observing the current HTTP standards. Apache has been the most popular web server ...
The DOCTYPE is a required preamble for HTML5 websites.
Allows a website to define how a page is rendered in Internet Explorer 8, allowing a website to decide to use IE7 style rendering over IE8 rendering.
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Meta descriptions are HTML attributes that provide concise explanations of the contents of web pages. Meta descriptions are commonly used on search engine result pages (SERPs) to display preview snippets for a given page.
The http-equiv attribute provides an HTTP header for the information/value of the content attribute. The http-equiv attribute can be used to simulate an HTTP response header.
A canonical link element is an HTML element that helps webmasters prevent duplicate content issues by specifying the "canonical", or "preferred".
The Open Graph protocol enables any web page to become a rich object in a social graph, a open protocol supported by Facebook
Open graph image entity tag


text: Was Anthony Albanese right about truck driver pay and safety? AAP Image/Joel Carrett April 13, 2016 FactCheck: do better pay rates for truck drivers improve safety? Michael Quinlan, UNSW Was Shadow Minister for Infrastructure and Transport, Anthony Albanese, right to say that evidence shows better pay for truck drivers will improve safety?


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A small red felt square held in place by a gold safety pin became the ubiquitous symbol of last year's student protest movement in Quebec against tuition-fee hikes.
Now, it's becoming a brand.
A small-business entrepreneur in the Montreal area has set up shop to market a line of polo shirts and tees sporting the famous red square.
Raymond Drapeau of Longueuil, Que. says he's not out to make a killing off of the logo but is simply keen to see the design gain more exposure.
"I saw the red felt squares and said to myself, 'we could broaden this a little bit and put it on shirts," said Mr. Drapeau, who has set up a website with the slogan "show your colours" to sell his apparel.
The budding businessman – he has a day job but refuses to disclose what it is – filed a trademark application for the design last year at the Canadian Intellectual Property Office and received tentative approval late last month.
Mr. Drapeau says he felt it was important for the commercial rights to be in Quebec and that he had to act before the light bulb went on somewhere else outside the province.
The logo got plenty of exposure last year in different high-profile venues. Quebec filmmaker Xavier Dolan wore it in his lapel on the red carpet at the Cannes film festival. Members of the popular Montreal-based indie band Arcade Fire had it pinned on while performing with Mick Jagger on Saturday Night Live.
Mr. Drapeau doesn't want to raise the ire of the student associations that launched the protests – and the emblem – and is interested in eventually working out a deal with them whereby they would receive part of the sale proceeds.
The students did not seem keen on grabbing a trademarketing opportunity, so he decided to plunge in, he said.
"This is my vision but not that of those who are against commercialization, against capitalism," he said.
"They might think they are the owners of the red square but in fact that is not the case," he said.
"It's unfortunate that an organization would say 'no, no, no, we're not going to get involved.'"
An official with one of the main student groups – Association pour une solidarité syndicale étudiante – said the matter is being studied and there won't be any comments for the moment.Report Typo/Error
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SIDNEY — A Sidney man was charged with aggravated robbery after Fuel Stop gas station on West Court Street reported an armed robbery on Wednesday morning.
Michael A. Sloan, 22, of Sidney, was arrested for aggravated robbery, and was taken to the Shelby County Jail after Sidney Police and Shelby County Sheriff Deputies responded to the report of the robbery at the gas station on Wednesday, Oct. 12 at 4 a.m.
According to the report, a male entered the business dressed in dark clothing with his face partially covered and brandishing a pistol. The suspect demanded money from the on duty clerk, who complied. The suspect then fled the store on foot and discarded his clothing and pistol as he ran.
Sloan was located within minutes of the call. The clothing and BB gun used in the robbery were recovered in close proximity to the gas station.


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Composer Gyorgy Ligeti, whose music was used in "2001: A Space Odyssey," died Monday at age 83 in Vienna. Ligeti was born to Hungarian Jewish parents in what is now Romania. His music studies were interrupted in 1943, when he was sent into forced labor until the end of World War II. He survived, though his father and brother were killed in the Holocaust.
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Shavuot is about breaking the tablets, and putting them back together


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Some things work really really well, like casting Robert Downey Jr. as Iron Man. Sure now it seems like he was the only choice but at the time Downey wasn't exactly in vogue in Hollywood....Read More →
Randy Orton was recently interviewed by To read the full interview, head over to THQ.com to promote the new WWE '12 video game. Here are some highlights of what Orton said about: His Favorite Rivalry...Read More →


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Phyllis A Gibbons (Age: 43)
11738 Farmingville NY
Phyllis A Gibbons (Age: 58)
32052 Jasper FL
Phyllis A Gibbons (Age: 68)
61068 Rochelle IL
Phyllis Gibbons, Jerome, US, E Ave, Unit C
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Phyllis GIBBONS - The Sydney Morning …
GIBBONS, Phyllis Constance. 22.02.1917 - 24.12.2015 Loved wife of Maurice (dec), loved mother of Petra, Paul (dec) and Louise. Loved mother-in-law of John ...
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Phyllis Gibbons | County of Siskiyou | Email @co.siskiyou.ca.us
Phyllis Gibbons is the Employment Coordinator of County of Siskiyou. Lead411's profile includes an email address, @co.siskiyou.ca.us, linkedin, biography, etc
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classmates: Phyllis Gibbons, Class of 1989 - Yreka …
Phyllis Gibbons 1989 graduate of Yreka High School in Yreka, CA is on Classmates.com. Get caught up with Phyllis Gibbons and other high school alumni …
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Yreka High School, Yreka, CA, 1985-1989
classmates: Phyllis (Cacioppo) Gibbons
Martin Van Buren High School, Queens Village, NY, 1959-1963
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Oskaloosa High School, Oskaloosa, IA, 1948-1952
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legacy.com: Phyllis E. Gibbons
GIBBONS, PHYLLIS E. In Sacramento, February 1, 2002. Beloved wife for 48 years of Francis "Gib" Gibbons. Loving sister of Paul Watschke. Dear aunt of Dan, Tom, Doug and Gary
legacy.com: Phyllis Gibbons-Neff
In Loving Memory of Phyllis Gibbons-Neff who passed away on September 15, 2011.
legacy.com: Phyllis Gibbons-Neff
Phyllis Gibbons-Neff, 91, died September 15, 2011. She was born in Wayne in 1919 and was the daughter of the late Charles and Eleanor Baker Barba. She grew up in Germantown and
findagrave: Gibbons, Phyllis E Gehring
05/1/1924 - 10/8/2001, Hamilton (Butler County, Ohio)
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Phyllis Elaine Gibbons (Despain) (1929 - 2006) - Genealogy
Genealogy for Phyllis Elaine Gibbons (Despain) (1929 - 2006) family tree on Geni, with over 150 million profiles of ancestors and living relatives.
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Gibbons · Phyllis Gibbons · Phyllis Gibbons · Phyllis Gibbons.
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Allied Occupation of Japan - Eiji Takemae - Google Books
Englishwoman Phyllis Gibbons, who had been with the family since Manila ...
Emerging Frontiers: Renewal in the Life of Women Religious : Sisters...
Founded in Indian Territory in 1858, the Sisters of Charity of Leavenworth met, a century later, challenges of a new frontier in the church?s call to adapt to...
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Tanzanian Women in Their Own Words is a compilation of oral histories by Tanzanian women living with disabilities or chronic illnesses. Beginning with their...
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In 2006, this reporter shed light on the seemingly unfair labor practices taking place in the Central American country of Costa Rica, in a factory operated by the Rawlings Sporting Goods Co., Inc., and now a subsidiary of the multi-national corporation, Jarden Corp.
As we embark upon the 2010 Major League Baseball (MLB) season, let us take another look back on this important issue regarding free trade and on that which has transpired since.
At that time, Rawlings was a subsidiary of K2, Inc., primarily a snowboard and in-line skate manufacturer. Then in 2007, Jarden absorbed all of K2's holdings and Rawlings became one of the many assets of Jarden's portfolio.
The Jarden Corp.'s holdings, prior to 2007, had primarily been in the consumer household goods industry, such as with Mr. Coffee, Oster, Holmes and CrockPot. It became pro-active in the purchase of outdoor clothing and camping equipment companies such as ExOfficio and Coleman and then with the purchase of K2, which owned Rawlings, Jarden became a force in the professional sporting goods industry as well.
But much like the way corporate takeovers can surface rapidly and on a global scale, with what appears as little hands-on management, corporations' goods are then subject to manufacture n far-off lands with little oversight, too.
And unfortunately, this accomplished strategy, having culminated primarily over the past 25 years, has enjoyed the muscle and delight of the U.S. government and other state governing bodies of countries throughout the world.
Unfortunately, global trade does little to improve the standard of living and human condition of the citizens living in such impoverished countries, where many global giants relocate.
Since this last report, to wit, Costa Rica has become a member of the Dominican Republic-Central American Free Trade Agreement (DR-CAFTA). Costa Rica, the oldest democracy in Central America, held a voters' referendum in 2007, giving its citizens a voice as to whether they would like to join DR-CAFTA.
The United States Congress rushed through DR-CAFTA in record time, over several months in 2005, but never expected a country such as Costa Rica to actually fight its demands or to obstruct its rush-through process; for all six other CAFTA countries El Salvador, Honduras, Nicaragua, Guatemala, and the Dominican Republic were all on board by 2007.
As it were, approval for DR-CAFTA was barely passed by Costa Rican voters, and it was not until January 1, 2009 that Costa Rica formally became another Free Trade Zone in Central America.
Few working for or playing in MLB, or for that matter most people living in the U.S., are aware that Free Trade Zones are but a win for the U.S. government and multi-national corporations operating offshore, only.
Such corporate entities are not required to pay taxes or tariffs, are allowed to import their supplies duty-free, and electricity and water usage are subsidized. Yet, they are not responsible or required to enforce labor and environmental policies, that would be required had they remained doing business in the U.S.
The following contains parts of the 2006 article, that encapsulates the story of Rawlings Sporting Goods, Inc. and its subsidiary, Rawlings de Costa Rica, S.A., and its manufacture of some 2.2 million baseballs each year made by hand. These laborers work for MLB's gain, its billionaire owners, and multi-millionaire players, who largely remain mum on this topic to date:
As America's National Pastime has continued to rake in record high revenues over the past several years in the billions of dollars each season MLB continues to remain deaf to its critics concerning the manufacture of its Official Baseball, apparel and other accessories, with regard to unfair labor practices in the Third World.
In 2004, a 60-page report produced by the National Labor Committee (NLC), an international labor rights organization, entitled, Foul Ball, initially exposed the poor working conditions of the Rawlings baseball factory in the remote city of Turrialba, Costa Rica.
MLB had a tepid response to such claims. Then, following the report, life-long consumer advocate, Ralph Nader, wrote a letter to both MLB Commissioner, Bud Selig, and then-Major League Baseball Players Association (MLBPA) Executive Director, Donald Fehr, to address Rawlings' labor practices. Selig referred Nader's letter to his legal department and Donald Fehr said he was unaware of such claims. Neither man ever followed up.


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SailNet Community (http://www.sailnet.com/forums/)
- Cruising & Liveaboard Forum (http://www.sailnet.com/forums/cruising-liveaboard-forum/)
- - Nova Scotia (http://www.sailnet.com/forums/cruising-liveaboard-forum/2259-nova-scotia.html)
Nova Scotia
I''ve just returned to Maine from sailing my Tartan 27 along some of the eastern coast of Nova Scotia. The trip was very successfulI. I did not have radar and went solo except for the overnight crossings of the Gulf of Maine. I''d be pleased to share my experiences with anyone who might be interested.
Nova Scotia
Your trip sounds verymuch like the one we are planning for next summer. I''d love to hear more about your trip. We live in New Hampshire; however, we moor our boat in Falmouth, ME. How much of your adventure can be done in two weeks? What''s your home port?
Sounds like quite a trip for a 27 footer.
I look forward to hearing from you.
Nova Scotia
I am also thinking of this trip for next year,God willing. I also sail solo. Would love to hear about your trip.
Nova Scotia
Mark, glad to hear that you''re planning a similar trip. As i said in my previous message, we sail out of Falmouth, ME. How about you? When areyou planning to go and for how long? We only have the last two weeks of July so we''ll have to make the most of it. SUSURROS, is our Morgan 415 so I won''t be sailing solo in fact my father-in-law is planning to come up fron Venezuela to make the trip. An open ocean adventure is something he has always wanted to do and he''s looking forward to it. Hpoe to hear about your plans.
Fair winds, Al
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2000 Wrangler good deal?
Got a question for you guys see what y'all think about this deal... been looking for a jeep for awhile now.. and just havent found the right 1...
Found this Jeep yesterday woundering how good a deal it really is...
2000 Jeep Wrangler SE has the I6 manual with 90k miles brand new bestop on it theres no major dmg on body..
Deal would be my 2000 dodge neon es (128k mile 4cylinder) + $2000 what u guys think?
I will take it. avarage price for the00 Neon $2,500 and for the
00 Jeep its about $10,000
I did something similar last year, I traded my 2000 Honda Prelude with 70k for a 2000 Sahara and I was even @ the dealership.
What they paid me for the Honda was the Same as what they where asking for the Jeep.
I couldn't be happier.
All SE models had the 4 cylinder. Are you sure you popped the hood and counted 6 plug wires? Do you have a VIN number?
Good point.
But still a good deal even if it is a 4 banger, I think.
its def a I6.. but yeah 3500 is price for the neon... so i geting jeep for $5500 just woundering now why its I6.. didnt even realize the 4banger SE deal..
get the VIN number. A simple phone call and you can run a carfax on it. You will be able to see exactly what is in it.
make sure the engine is legit.. dont wanna buy stolen property.. make sure if it is an I6 in there, have him sign a mini contract that its not stolen or the possibiity there of it..
but yeah ita a smoken deal.. if everything works.
sry for late reply.. 2000 jeep did not work out.. looking at a '98 jeep wrangler 148k miles now thats on a car dealer... talked to old owner and traded in cause not enuff room for growing fam.. its a I4 stick.. has 4" suspension lift and 35s tires... its been regeared for the 35s.. worried about the 4banger pulling those tires... also their trying to say jeep gets 18mpg... i dont see that happening... what u guys think?? price is setting at 6k atm.. sound like a good deal?
no 4 cylinder can be geared for 35s without new axles. :D No way it gets 18. Maybe without a speedo correction.
32 Attachment(s)
if you want some +33" tires you should buy a 6cyl.
and the i6 also makes much more fun.
Don't buy the 98 wrangler you can get a better deal. I bought a 98 sport 4.0 and 5 speed for $3500 this summer. Jeep has 160,000 miles and runs great. About the 35s and 4 banger forget about it. Get a 6 cyl jeep.
went tuesday and drove the jeep.. tires rub like crazy... and with it being a I4 with a d30 in it.. i dont see it holding up too those 35s.. :eek: guess my search continues for a jeep... hopefully ill find a I6 around here for decent price before long...
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text: Data Mining: Concepts and Techniques Jiawei Han and Micheline Kamber October 7, 2015 Data Mining: Concepts and Techniques 1 Chapter 1. Introduction Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Classification of data mining systems Top-10 most popular data mining algorithms Major issues in data mining Overview of the course October 7, 2015 Data Mining: Concepts and Techniques 2 Why Data Mining? The Explosive Growth of Data: from terabytes to petabytes Data collection and data availability Automated data collection tools, database systems, Web, computerized society Major sources of abundant data Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube We are drowning in data, but starving for knowledge! "Necessity is the mother of invention"—Data mining—Automated analysis of massive data sets October 7, 2015 Data Mining: Concepts and Techniques 3 Evolution of Sciences Before 1600, empirical science 1600-1950s, theoretical science 1950s-1990s, computational science Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.) Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models. 1990-now, data science The flood of data from new scientific instruments and simulations The ability to economically store and manage petabytes of data online The Internet and computing Grid that makes all these archives universally accessible Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge! Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov. 2002 October 7, 2015 Data Mining: Concepts and Techniques 4 Evolution of Database Technology 1960s: 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s: Data collection, database creation, IMS and network DBMS Data mining, data warehousing, multimedia databases, and Web databases 2000s Stream data management and mining Data mining and its applications Web technology (XML, data integration) and global information systems October 7, 2015 Data Mining: Concepts and Techniques 5 What Is Data Mining? Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Alternative names Data mining: a misnomer? Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. Watch out: Is everything "data mining"? Simple search and query processing (Deductive) expert systems October 7, 2015 Data Mining: Concepts and Techniques 6 Knowledge Discovery (KDD) Process Data mining—core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases October 7, 2015 Data Mining: Concepts and Techniques 7 Data Mining and Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques End User Business Analyst Data Mining Information Discovery Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems October 7, 2015 Data Mining: Concepts and Techniques DBA 8 Data Mining: Confluence of Multiple Disciplines Database Technology Machine Learning Pattern Recognition October 7, 2015 Statistics Data Mining Algorithm Data Mining: Concepts and Techniques Visualization Other Disciplines 9 Why Not Traditional Data Analysis? Tremendous amount of data High-dimensionality of data Algorithms must be highly scalable to handle such as tera-bytes of data Micro-array may have tens of thousands of dimensions High complexity of data Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social networks and multi-linked data Heterogeneous databases and legacy databases Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations New and sophisticated applications October 7, 2015 Data Mining: Concepts and Techniques 10 Multi-Dimensional View of Data Mining Data to be mined Knowledge to be mined Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Techniques utilized Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Applications adapted October 7, 2015 Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. Data Mining: Concepts and Techniques 11 Data Mining: Classification Schemes General functionality Descriptive data mining Predictive data mining Different views lead to different classifications Data view: Kinds of data to be mined Knowledge view: Kinds of knowledge to be discovered Method view: Kinds of techniques utilized Application view: Kinds of applications adapted October 7, 2015 Data Mining: Concepts and Techniques 12 Data Mining: On What Kinds of Data? Database-oriented data sets and applications Relational database, data warehouse, transactional database Advanced data sets and advanced applications Data streams and sensor data Time-series data, temporal data, sequence data (incl. bio-sequences) Structure data, graphs, social networks and multi-linked data Object-relational databases Heterogeneous databases and legacy databases Spatial data and spatiotemporal data Multimedia database Text databases The World-Wide Web October 7, 2015 Data Mining: Concepts and Techniques 13 Data Mining Functionalities Multidimensional concept description: Characterization and discrimination Frequent patterns, association, correlation vs. causality Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Diaper Beer [0.5%, 75%] (Correlation or causality?) Classification and prediction Construct models (functions) that describe and distinguish classes or concepts for future prediction October 7, 2015 E.g., classify countries based on (climate), or classify cars based on (gas mileage) Predict some unknown or missing numerical values Data Mining: Concepts and Techniques 14 Data Mining Functionalities (2) Cluster analysis Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier analysis Outlier: Data object that does not comply with the general behavior of the data Noise or exception? Useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: e.g., regression analysis Sequential pattern mining: e.g., digital camera large SD memory Periodicity analysis Similarity-based analysis Other pattern-directed or statistical analyses October 7, 2015 Data Mining: Concepts and Techniques 15 Top-10 Most Popular DM Algorithms: 18 Identified Candidates (I) Classification #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993. #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984. #3. K Nearest Neighbours (kNN): Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6) #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398. Statistical Learning #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag. #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94. #8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00. October 7, 2015 Data Mining: Concepts and Techniques 16 The 18 Identified Candidates (II) Link Mining #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW-7, 1998. #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998. Clustering #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967. #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96. Bagging and Boosting #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decisiontheoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139. October 7, 2015 Data Mining: Concepts and Techniques 17 The 18 Identified Candidates (III) Sequential Patterns #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, 1996. #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01. Integrated Mining #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98. Rough Sets #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992 Graph Mining #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM '02. October 7, 2015 Data Mining: Concepts and Techniques 18 Top-10 Algorithm Finally Selected at ICDM'06 #1: C4.5 (61 votes) #2: K-Means (60 votes) #3: SVM (58 votes) #4: Apriori (52 votes) #5: EM (48 votes) #6: PageRank (46 votes) #7: AdaBoost (45 votes) #7: kNN (45 votes) #7: Naive Bayes (45 votes) #10: CART (34 votes) October 7, 2015 Data Mining: Concepts and Techniques 19 Major Issues in Data Mining Mining methodology Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one: knowledge fusion User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of abstraction Applications and social impacts October 7, 2015 Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy Data Mining: Concepts and Techniques 20 A Brief History of Data Mining Society 1989 IJCAI Workshop on Knowledge Discovery in Databases 1991-1994 Workshops on Knowledge Discovery in Databases Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996) 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD'95-98) Journal of Data Mining and Knowledge Discovery (1997) ACM SIGKDD conferences since 1998 and SIGKDD Explorations More conferences on data mining PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc. ACM Transactions on KDD starting in 2007 October 7, 2015 Data Mining: Concepts and Techniques 21 Conferences and Journals on Data Mining KDD Conferences ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) SIAM Data Mining Conf. (SDM) (IEEE) Int. Conf. on Data Mining (ICDM) Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD) Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) October 7, 2015 Other related conferences ACM SIGMOD VLDB (IEEE) ICDE WWW, SIGIR ICML, CVPR, NIPS Journals Data Mining and Knowledge Discovery (DAMI or DMKD) IEEE Trans. On Knowledge and Data Eng. (TKDE) KDD Explorations ACM Trans. on KDD Data Mining: Concepts and Techniques 22 Where to Find References? DBLP, CiteSeer, Google Data mining and KDD (SIGKDD: CDROM) Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM) Conferences: SIGIR, WWW, CIKM, etc. Journals: WWW: Internet and Web Information Systems, Statistics Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc. Web and IR Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc. AI & Machine Learning Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Conferences: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization October 7, 2015 Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc. Data Mining: Concepts and Techniques 23 Recommended Reference Books S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996 U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006 D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001 B. Liu, Web Data Mining, Springer 2006. T. M. Mitchell, Machine Learning, McGraw Hill, 1997 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005 October 7, 2015 Data Mining: Concepts and Techniques 24 Summary Data mining: Discovering interesting patterns from large amounts of data A natural evolution of database technology, in great demand, with wide applications A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation Mining can be performed in a variety of information repositories Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. Data mining systems and architectures Major issues in data mining October 7, 2015 Data Mining: Concepts and Techniques 25 Supplementary Lecture Slides Note: The slides following the end of chapter summary are supplementary slides that could be useful for supplementary readings or teaching These slides may have its corresponding text contents in the book chapters, but were omitted due to limited time in author's own course lecture The slides in other chapters have similar convention and treatment October 7, 2015 Data Mining: Concepts and Techniques 26 Why Data Mining?—Potential Applications Data analysis and decision support Market analysis and management Risk analysis and management Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Other Applications Text mining (news group, email, documents) and Web mining Stream data mining Bioinformatics and bio-data analysis October 7, 2015 Data Mining: Concepts and Techniques 27 Ex. 1: Market Analysis and Management Where does the data come from?—Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing Find clusters of "model" customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association Customer profiling—What types of customers buy what products (clustering or classification) Customer requirement analysis Identify the best products for different groups of customers Predict what factors will attract new customers Provision of summary information Multidimensional summary reports Statistical summary information (data central tendency and variation) October 7, 2015 Data Mining: Concepts and Techniques 28 Ex. 2: Corporate Analysis & Risk Management Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning summarize and compare the resources and spending Competition monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in a highly competitive market October 7, 2015 Data Mining: Concepts and Techniques 29 Ex. 3: Fraud Detection & Mining Unusual Patterns Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. Auto insurance: ring of collisions Money laundering: suspicious monetary transactions Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud Retail industry October 7, 2015 Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism Data Mining: Concepts and Techniques 30 KDD Process: Several Key Steps Learning the application domain relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation Find useful features, dimensionality/variable reduction, invariant representation Choosing functions of data mining summarization, classification, regression, association, clustering Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge October 7, 2015 Data Mining: Concepts and Techniques 31 Are All the "Discovered" Patterns Interesting? Data mining may generate thousands of patterns: Not all of them are interesting Suggested approach: Human-centered, query-based, focused mining Interestingness measures A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm Objective vs. subjective interestingness measures Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. Subjective: based on user's belief in the data, e.g., unexpectedness, novelty, actionability, etc. October 7, 2015 Data Mining: Concepts and Techniques 32 Find All and Only Interesting Patterns? Find all the interesting patterns: Completeness Can a data mining system find all the interesting patterns? Do we need to find all of the interesting patterns? Heuristic vs. exhaustive search Association vs. classification vs. clustering Search for only interesting patterns: An optimization problem Can a data mining system find only the interesting patterns? Approaches October 7, 2015 First general all the patterns and then filter out the uninteresting ones Generate only the interesting patterns—mining query optimization Data Mining: Concepts and Techniques 33 Other Pattern Mining Issues Precise patterns vs. approximate patterns Association and correlation mining: possible find sets of precise patterns But approximate patterns can be more compact and sufficient How to find high quality approximate patterns?? Gene sequence mining: approximate patterns are inherent How to derive efficient approximate pattern mining algorithms?? Constrained vs. non-constrained patterns October 7, 2015 Why constraint-based mining? What are the possible kinds of constraints? How to push constraints into the mining process? Data Mining: Concepts and Techniques 34 A Few Announcements (Sept. 1) A new section CS412ADD: CRN 48711 and its rules/arrangements 4th Unit for I2CS students Survey report for mining new types of data 4th Unit for in-campus students October 7, 2015 High quality implementation of one selected (to be discussed with TA/Instructor) data mining algorithm in the textbook Or, a research report if you plan to devote your future research thesis on data mining Data Mining: Concepts and Techniques 35 Why Data Mining Query Language? Automated vs. query-driven? Data mining should be an interactive process Finding all the patterns autonomously in a database?—unrealistic because the patterns could be too many but uninteresting User directs what to be mined Users must be provided with a set of primitives to be used to communicate with the data mining system Incorporating these primitives in a data mining query language More flexible user interaction Foundation for design of graphical user interface Standardization of data mining industry and practice October 7, 2015 Data Mining: Concepts and Techniques 36 Primitives that Define a Data Mining Task Task-relevant data Database or data warehouse name Database tables or data warehouse cubes Condition for data selection Relevant attributes or dimensions Data grouping criteria Type of knowledge to be mined Characterization, discrimination, association, classification, prediction, clustering, outlier analysis, other data mining tasks Background knowledge Pattern interestingness measurements Visualization/presentation of discovered patterns October 7, 2015 Data Mining: Concepts and Techniques 37 Primitive 3: Background Knowledge A typical kind of background knowledge: Concept hierarchies Schema hierarchy Set-grouping hierarchy E.g., street < city < province_or_state < country E.g., {20-39} = young, {40-59} = middle_aged Operation-derived hierarchy email address: firstname.lastname@example.com login-name < department < university < country Rule-based hierarchy October 7, 2015 low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 P2) < $50 Data Mining: Concepts and Techniques 38 Primitive 4: Pattern Interestingness Measure Simplicity e.g., (association) rule length, (decision) tree size Certainty e.g., confidence, P(A|B) = #(A and B)/ #(B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc. Utility potential usefulness, e.g., support (association), noise threshold (description) Novelty not previously known, surprising (used to remove redundant rules, e.g., Illinois vs. Champaign rule implication support ratio) October 7, 2015 Data Mining: Concepts and Techniques 39 Primitive 5: Presentation of Discovered Patterns Different backgrounds/usages may require different forms of representation E.g., rules, tables, crosstabs, pie/bar chart, etc. Concept hierarchy is also important Discovered knowledge might be more understandable when represented at high level of abstraction Interactive drill up/down, pivoting, slicing and dicing provide different perspectives to data Different kinds of knowledge require different representation: association, classification, clustering, etc. October 7, 2015 Data Mining: Concepts and Techniques 40 DMQL—A Data Mining Query Language Motivation A DMQL can provide the ability to support ad-hoc and interactive data mining By providing a standardized language like SQL Hope to achieve a similar effect like that SQL has on relational database Foundation for system development and evolution Facilitate information exchange, technology transfer, commercialization and wide acceptance Design October 7, 2015 DMQL is designed with the primitives described earlier Data Mining: Concepts and Techniques 41 An Example Query in DMQL October 7, 2015 Data Mining: Concepts and Techniques 42 Other Data Mining Languages & Standardization Efforts Association rule language specifications MSQL (Imielinski & Virmani'99) MineRule (Meo Psaila and Ceri'96) Query flocks based on Datalog syntax (Tsur et al'98) OLEDB for DM (Microsoft'2000) and recently DMX (Microsoft SQLServer 2005) Based on OLE, OLE DB, OLE DB for OLAP, C# Integrating DBMS, data warehouse and data mining DMML (Data Mining Mark-up Language) by DMG (www.dmg.org) Providing a platform and process structure for effective data mining Emphasizing on deploying data mining technology to solve business problems October 7, 2015 Data Mining: Concepts and Techniques 43 Integration of Data Mining and Data Warehousing Data mining systems, DBMS, Data warehouse systems coupling On-line analytical mining data No coupling, loose-coupling, semi-tight-coupling, tight-coupling integration of mining and OLAP technologies Interactive mining multi-level knowledge Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc. Integration of multiple mining functions October 7, 2015 Characterized classification, first clustering and then association Data Mining: Concepts and Techniques 44 Coupling Data Mining with DB/DW Systems No coupling—flat file processing, not recommended Loose coupling Semi-tight coupling—enhanced DM performance Fetching data from DB/DW Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions Tight coupling—A uniform information processing environment October 7, 2015 DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc. Data Mining: Concepts and Techniques 45 Architecture: Typical Data Mining System Graphical User Interface Pattern Evaluation Data Mining Engine Knowl edgeBase Database or Data Warehouse Server data cleaning, integration, and selection Database October 7, 2015 Data World-Wide Other Info Repositories Warehouse Web Data Mining: Concepts and Techniques 46


text:
Can I get mine with chromed out twenty-twos?
Segway (Repost)
By Kickntrue on 8/4/07
Something reminded me of this post and I thought I'd repost it. I want one. Check it here.
These make a lot more sense than golf carts. When oobolf International opens up, we are definately doing these instead of carts. Think of the speed of play!
If you golf around Hilton Head you can try these out at Palmetto Dunes
[ comments ]
mbills1015 says:
that is fantastic!!! I saw Shaq had one on an episode of cribs. I would love to see my grandfather try and use one of them.
klangdon says:
Speed of play, better for the turf, and more fun for golfers. Assuming the pricing is not outragious these appear to be no brainers for courses looking to upgrade.
jimithen says:
I feel this is a much more efficient way to play with groups of people. Using your own Segway to get to your own ball. not to mention the wheelies you could pop with these things.
klangdon says:
Another benefit I just thought of is that it would be easier to find balls that stray into the trees.
falcon50driver says:
I wonder if you could get some hubcaps that continue to spin when the thing comes to a stop. Or maybe a tiny steering wheel made of chrome chain welded together, So that you could still drive it, even though you had handcuffs on.
volleyhart says:
that is pretty freakin' cool
[ post comment ]
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