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Quality Measures in Data Mining

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Quality Measures in Data Mining Synopsis

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.

About This Edition

ISBN: 9783642079528
Publication date:
Author: Fabrice Guillet, Howard J Hamilton
Publisher: Springer an imprint of Springer Berlin Heidelberg
Format: Paperback
Pagination: 314 pages
Series: Studies in Computational Intelligence
Genres: Maths for engineers
Artificial intelligence