Studies in Computational Intelligence

By: Fabrice Guillet (Editor), Howard J. Hamilton (Editor)

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Hardcover | 8 January 2007

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Hardcover
328 Pages

Dimensions(cm)
22.86 x 15.24 x 1.91

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Mathematics Applied Mathematics Computer Science Artificial Intelligence Data Capture & Analysis Engineering & Technology Technology in General Engineering in General

Maths for Engineers

Overviews on rule quality
Choosing the Right Lens: Finding What is Interesting in Data Mining	p. 3
A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool a Comparative Study	p. 25
Association Rule Interestingness Measures: Experimental and Theoretical Studies	p. 51
On the Discovery of Exception Rules: A Survey	p. 77
From data to rule quality
Measuring and Modelling Data Quality for Quality-Awareness in Data Mining	p. 101
Quality and Complexity Measures for Data Linkage and Deduplication	p. 127
Statistical Methodologies for Mining Potentially Interesting Contrast Sets	p. 153
Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality	p. 179
Rule quality and validation
A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link	p. 207
Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules	p. 237
Association Rule Interestingness: Measure and Statistical Validation	p. 251
Comparing Classification Results between N-ary and Binary Problems	p. 277
About the Authors	p. 303
Index	p. 311
Table of Contents provided by Publisher. All Rights Reserved.

Studies in Computational Intelligence

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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