<div class="description">
<dl><dd>Thesis advisor(s): B. Ramesh ; William R. Gates</dd>
<dd>"December 1993."</dd>
<dd>Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1993</dd>
<dd>Includes bibliographical references</dd>
<dd>Dramatic growth in database technology has outpaced the ability to analyze the information stored in databases for new knowledge and has created an increasing potential for the loss of undiscovered knowledge. This potential gains for such knowledge discovery are particularly large in the Department of Defense where millions of transactions, from maintenance to medical information, are recorded yearly. Due to the limitations of traditional knowledge discovery methods in analyzing this data, there is a growing need to utilize new knowledge discovery methods to glean knowledge from vast databases. This research compares a new knowledge discovery approach using a genetic program (GP) developed at the Naval Postgraduate School that produces data associations expressed as IF X THEN Y rules. In determining validity of this GP approach, the program is compared to traditional statistical and inductive methods of knowledge discovery. Results of this comparison indicate the viability of using a GP approach in knowledge discovery by three findings. First, the GP discovered interesting patterns from the data set. Second, the GP discovered new relationships not uncovered by the traditional methods. Third, the GP demonstrated a greater ability to focus the knowledge discovery search towards particular relationships, such as producing exact or general rules</dd>
<dd>Mode of access: World Wide Web</dd>
<dd>System requirements: Adobe Acrobat Reader</dd>
<dd>US Navy (USN) author</dd>
<dd>Bahrain Defense Force author</dd></dl><br>Subjects:</div>
<div class="fn value">
Smith, Steven Lee.;Al-Mahmood, Mohammed Abdul latif.</div>
Internet Archive identifier