Example DATA MINING – 

 Visualization 

 Challenge & Selection      Data mining is very powerful , but it faces many challenges during its   implementation . The challenges could be related to performance , data ,   methods and techniques used etc . The data mining process becomes successful   when the challenges or issues are identified correctly and sorted out properly .   Data mining normally leads to serious issues in terms of data security , privacy   and governance . For example , when a retailer analyzes the purchase details , it   reveals information about buying habits and preferences of customers without   their permission .     Although data mining is still in its infancy , companies in a wide range of   industries - including retail , finance , heath care , manufacturing transportation ,   and aerospace - are already using data mining tools and techniques to take   advantage of historical data . By using pattern recognition technologies and   statistical and mathematical techniques to sift through warehoused   information , data mining helps analysts recognize significant facts ,   relationships , trends , patterns , exceptions and anomalies that might otherwise   go unnoticed .     It is difficult to represent the information in an accurate and easy - to - understand   way to the end user . The input data and output information being complex ,   very effective and successful data visualization techniques need to be applied to   make it successful .    There are many more challenges in data mining in addition to the above   specified issues . More challenges get revealed as the actual data mining process   starts and the success of data mining lies in overcoming all these challenges        Application      Data Mining is primarily used today by companies with a strong consumer focus   — retail , financial , communication , and marketing organizations , to “ drill down ”   into their transactional data and determine pricing , customer preferences and 

 product positioning , impact on sales , customer satisfaction and corporate   profits . With data mining , a retailer can use point - of - sale records of customer   purchases to develop products and promotions to appeal to specific customer   segments .      The final step of knowledge discovery from data is to verify that the patterns   produced by the data mining algorithms occur in the wider data set . Not all   patterns found by data mining algorithms are necessarily valid . It is common for   data mining algorithms to find patterns in the training set which are not present   in the general data set . This is called overfitting . To overcome this , the   evaluation uses a test set of data on which the data mining algorithm was not   trained . The learned patterns are applied to this test set , and the resulting   output is compared to the desired output . For example , a data mining algorithm   trying to distinguish " spam " from " legitimate " emails would be trained on   a training set of sample e - mails . Once trained , the learned patterns would be   applied to the test set of e - mails on which it had not been trained . The accuracy   of the patterns can then be measured from how many e - mails they correctly   classify . Several statistical methods may be used to evaluate the algorithm , such   as ROC curves .      For the activity , I will divide the stakeholders into groups of 5 - 6 participants   each representing a different organization or area of interest . Each group   receives a problem and situation on data mining graphs that they can write on   directly or use sticky notes to make comments . I give them two - three questions   to answer , such as : 1 ) What key elements are missing from the situation ? 2 )   What would you change about the data mining problems to make it more   accurately reflect your problem space ? 3 ) What key insights do you have about   the relationships between the entities in your problem space ? I give them   approximately 20 minutes to discuss the questions and mark up the graphic .   Then I have each group report out on their answers to those three questions   while pointing to the mark - ups on their poster . As each group reports out , I   record commonalities in the insights of each group , and then additional insights   that come from the group discussion on an additional copy of the graphic . After   the session , I digitize each group ’s poster as an artifact , create a view that   combines the shared and new insights from the group discussion , and then   create a revised graphic that incorporates their feedback and proposed changes   that they agreed to as a group . 

 Insight & Approach      Businesses which have been slow in adopting the process of data mining are   now catching up with the others . Extracting important information through   the process of data mining is widely used to make critical business decisions .   In the coming decade , we can expect data mining to become as ubiquitous   as some of the more prevalent technologies used today . Some of the key   data mining trends for the future include 

 Data mining is used to discover patterns and relationships in the data in order to   help make better business decisions . Data mining can help spot sales trends ,   develop smarter marketing campaigns , and accurately predict customer loyalty .   Specific uses of data mining include : 

  Market segmentation - Identify the common characteristics of customers 

 who buy the same products from your company . 

  Customer churn - Predict which customers are likely to leave your company 

 and go to a competitor . 

  Fraud detection - Identify which transactions are most likely to be 

 fraudulent . 

  Direct marketing - Identify which prospects should be included in a mailing 

 list to obtain the highest response rate . 

  Interactive marketing - Predict what each individual accessing a Web site is 

 most likely interested in seeing . 

  Market basket analysis - Understand what products or services are 

 commonly purchased together ; e.g. , beer and diapers . 

  Trend analysis - Reveal the difference between a typical customer this 

 month and last . 

 In the short - term , the results of data mining will be in profitable , if mundane ,   business related areas . Micro - marketing campaigns will explore new niches .   Advertising will target potential customers with new precision . 

 In the medium term , data mining may be as common and easy to use as e - mail .   We may use these tools to find the best airfare to New York , root out a phone   number of a long - lost classmate , or find the best prices on lawn mowers . 

 The long - term prospects are truly exciting . Imagine intelligent agents turned loose   on medical research data or on sub - atomic particle data . Computers may reveal   new treatments for diseases or new insights into the nature of the universe . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Example DATA MINING – 

 Visualization 

 Challenge & Selection      Data mining is very powerful , but it faces many challenges during its   implementation . The challenges could be related to performance , data ,   methods and techniques used etc . The data mining process becomes successful   when the challenges or issues are identified correctly and sorted out properly .   Data mining normally leads to serious issues in terms of data security , privacy   and governance . For example , when a retailer analyzes the purchase details , it   reveals information about buying habits and preferences of customers without   their permission .     Although data mining is still in its infancy , companies in a wide range of   industries - including retail , finance , heath care , manufacturing transportation ,   and aerospace - are already using data mining tools and techniques to take   advantage of historical data . By using pattern recognition technologies and   statistical and mathematical techniques to sift through warehoused   information , data mining helps analysts recognize significant facts ,   relationships , trends , patterns , exceptions and anomalies that might otherwise   go unnoticed .     It is difficult to represent the information in an accurate and easy - to - understand   way to the end user . The input data and output information being complex ,   very effective and successful data visualization techniques need to be applied to   make it successful .    There are many more challenges in data mining in addition to the above   specified issues . More challenges get revealed as the actual data mining process   starts and the success of data mining lies in overcoming all these challenges        Application      Data Mining is primarily used today by companies with a strong consumer focus   — retail , financial , communication , and marketing organizations , to “ drill down ”   into their transactional data and determine pricing , customer preferences and 

 product positioning , impact on sales , customer satisfaction and corporate   profits . With data mining , a retailer can use point - of - sale records of customer   purchases to develop products and promotions to appeal to specific customer   segments .      The final step of knowledge discovery from data is to verify that the patterns   produced by the data mining algorithms occur in the wider data set . Not all   patterns found by data mining algorithms are necessarily valid . It is common for   data mining algorithms to find patterns in the training set which are not present   in the general data set . This is called overfitting . To overcome this , the   evaluation uses a test set of data on which the data mining algorithm was not   trained . The learned patterns are applied to this test set , and the resulting   output is compared to the desired output . For example , a data mining algorithm   trying to distinguish " spam " from " legitimate " emails would be trained on   a training set of sample e - mails . Once trained , the learned patterns would be   applied to the test set of e - mails on which it had not been trained . The accuracy   of the patterns can then be measured from how many e - mails they correctly   classify . Several statistical methods may be used to evaluate the algorithm , such   as ROC curves .      For the activity , I will divide the stakeholders into groups of 5 - 6 participants   each representing a different organization or area of interest . Each group   receives a problem and situation on data mining graphs that they can write on   directly or use sticky notes to make comments . I give them two - three questions   to answer , such as : 1 ) What key elements are missing from the situation ? 2 )   What would you change about the data mining problems to make it more   accurately reflect your problem space ? 3 ) What key insights do you have about   the relationships between the entities in your problem space ? I give them   approximately 20 minutes to discuss the questions and mark up the graphic .   Then I have each group report out on their answers to those three questions   while pointing to the mark - ups on their poster . As each group reports out , I   record commonalities in the insights of each group , and then additional insights   that come from the group discussion on an additional copy of the graphic . After   the session , I digitize each group ’s poster as an artifact , create a view that   combines the shared and new insights from the group discussion , and then   create a revised graphic that incorporates their feedback and proposed changes   that they agreed to as a group . 

 Insight & Approach      Businesses which have been slow in adopting the process of data mining are   now catching up with the others . Extracting important information through   the process of data mining is widely used to make critical business decisions .   In the coming decade , we can expect data mining to become as ubiquitous   as some of the more prevalent technologies used today . Some of the key   data mining trends for the future include 

 Data mining is used to discover patterns and relationships in the data in order to   help make better business decisions . Data mining can help spot sales trends ,   develop smarter marketing campaigns , and accurately predict customer loyalty .   Specific uses of data mining include : 

  Market segmentation - Identify the common characteristics of customers 

 who buy the same products from your company . 

  Customer churn - Predict which customers are likely to leave your company 

 and go to a competitor . 

  Fraud detection - Identify which transactions are most likely to be 

 fraudulent . 

  Direct marketing - Identify which prospects should be included in a mailing 

 list to obtain the highest response rate . 

  Interactive marketing - Predict what each individual accessing a Web site is 

 most likely interested in seeing . 

  Market basket analysis - Understand what products or services are 

 commonly purchased together ; e.g. , beer and diapers . 

  Trend analysis - Reveal the difference between a typical customer this 

 month and last . 

 In the short - term , the results of data mining will be in profitable , if mundane ,   business related areas . Micro - marketing campaigns will explore new niches .   Advertising will target potential customers with new precision . 

 In the medium term , data mining may be as common and easy to use as e - mail .   We may use these tools to find the best airfare to New York , root out a phone   number of a long - lost classmate , or find the best prices on lawn mowers . 

 The long - term prospects are truly exciting . Imagine intelligent agents turned loose   on medical research data or on sub - atomic particle data . Computers may reveal   new treatments for diseases or new insights into the nature of the universe .
### Output: 