Quality Measures in Data Mining

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.
Written for:

Researchers, engineers, graduate students in Computational Intelligence and Computer Science

Download link:


[Fast Download] Quality Measures in Data Mining

Ebooks related to "Quality Measures in Data Mining" :
iPad: The Missing Manual
iPod: The Missing Manual
iPod: The Missing Manual
CompTIA A+ Certification All-in-One Exam Guide, Ninth Edition (Exams 220-901 & 220-902)
The Circuits and Filters Handbook, Second Edition by Wai-Kai Chen - 2002
Building a Media Center with Raspberry Pi
Computer Organization and Architecture, 9th Edition
PCs For Dummies (10th edition)
MacBook Pro 2010 Service Manuals
Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.