Guillet F., Hamilton H.J. (eds.) Quality Measures in Data Mining

New York: Springer, 2007. — 313 p. — ISBN: 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.Overviews on rule quality
Liqiang Geng, Howard J. Hamilton
Choosing the Right Lens: Finding What is Interesting in Data Mining
Xuan-Hiep Huynh, Fabrice Guillet, Julien Blanchard, Pascale Kuntz, Henri Briand, Régis Gras
A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study
Philippe Lenca, Benoît Vaillant, Patrick Meyer, Stéphane Lallich
Association Rule Interestingness Measures: Experimental and Theoretical Studies
Béatrice Duval, Ansaf Salleb, Christel Vrain
On the Discovery of Exception Rules: A Survey
From data to rule quality
Laure Berti-Equillé
Measuring and Modeling Data Quality for Quality-Awareness in Data Mining
Peter Christen, Karl Goiser
Quality and Complexity Measures for Data Linkage and Deduplication
Robert J. Hilderman, Terry Peckham
Statistical Methodologies for Mining Potentially Interesting Contrast Sets
Rajesh Natarajan, B. Shekar
Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality
Rule quality and validation
Israël-César Lerman, Jérôme Azé
A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link
Jean Diatta, Henri Ralambondrainy, André Totohasina
Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules
Stephane Lallich, Olivier Teytaud, Elie Prudhomme
Association Rule Interestingness: Measure and Statistical Validation
Mary Felkin
Comparing Classification Results between N-ary and Binary Problems