Quality Measures in Data Mining

Liqiang Geng, Howard J. Hamilton (auth.), Fabrice J. Guillet, Howard J. Hamilton (eds.)9783540449119, 3-540-44911-6

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.

Table of contents :
Front Matter….Pages I-XIV
Front Matter….Pages 1-1
Choosing the Right Lens: Finding What is Interesting in Data Mining….Pages 3-24
A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study….Pages 25-50
Association Rule Interestingness Measures: Experimental and Theoretical Studies….Pages 51-76
On the Discovery of Exception Rules: A Survey….Pages 77-98
Front Matter….Pages 100-100
Measuring and Modelling Data Quality for Quality-Awareness in Data Mining….Pages 101-126
Quality and Complexity Measures for Data Linkage and Deduplication….Pages 127-151
Statistical Methodologies for Mining Potentially Interesting Contrast Sets….Pages 153-177
Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality….Pages 179-203
Front Matter….Pages 206-206
A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link….Pages 207-236
Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules….Pages 237-250
Association Rule Interestingness: Measure and Statistical Validation….Pages 251-275
Comparing Classification Results between N-ary and Binary Problems….Pages 277-301
Back Matter….Pages 303-313