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ORCIDQuality Measures in Data Mining 2007
- Fabrice Guillet, Howard J. Hamilton:
Quality Measures in Data Mining. Studies in Computational Intelligence 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-Équille:
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
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