Quality Measures in Data Mining (Studies in Computational Intelligence)
by Fabrice Guillet and Fabrice Guillet;Howard J. Hamilton
Sales Rank: 2623544
$217.02 At Amazon
Hardcover: 327 pages
Publisher: Springer; 1 edition February 21, 2007
Language: English
ISBN-10: 3540449116
ISBN-13: 978-3540449119
Product Dimensions:
9.3 x 6.2 x 0.9 inches
Shipping Weight: 1.2 pounds
Product Description
Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.
This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.
