Quality Measures in Data Mining 2007

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