Quality Measures in Data Mining 2007
Fabrice Guillet, Howard J. Hamilton (Eds.):
Quality Measures in Data Mining.
Studies in Computational Intelligence Vol. 43 Springer 2007, ISBN 978-3-540-44911-9 
- Liqiang Geng, Howard J. Hamilton:
Choosing the Right Lens: Finding What is Interesting in Data Mining.
3-24
- Hiep Xuan 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.
25-50
- Philippe Lenca, Benoît Vaillant, Patrick Meyer, Stéphane Lallich:
Association Rule Interestingness Measures: Experimental and Theoretical Studies.
51-76
- Béatrice Duval, Ansaf Salleb, Christel Vrain:
On the Discovery of Exception Rules: A Survey.
77-98
- Laure Berti-Equille:
Measuring and Modelling Data Quality for Quality-Awareness in Data Mining.
101-126
- Peter Christen, Karl Goiser:
Quality and Complexity Measures for Data Linkage and Deduplication.
127-151
- Robert J. Hilderman, Terry Peckham:
Statistical Methodologies for Mining Potentially Interesting Contrast Sets.
153-177
- Rajesh Natarajan, B. Shekar:
Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality.
179-203
- Israël-César Lerman, Jérôme Azé:
A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link.
207-236
- Jean Diatta, Henri Ralambondrainy, André Totohasina:
Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules.
237-250
- Stéphane Lallich, Olivier Teytaud, Elie Prudhomme:
Association Rule Interestingness: Measure and Statistical Validation.
251-275
- Mary Felkin:
Comparing Classification Results between N-ary and Binary Problems.
277-301
Last update Mon Feb 13 04:17:13 2012
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