Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of association rules using closed itemset lattices
Information Systems
Context-Based Similarity Measures for Categorical Databases
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A Theoretical Framework for Decision Trees in Uncertain Domains: Application to Medical Data Sets
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
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The production of suitable clusters to help physicians explore data and take decisions is a hard task. This paper addresses this question and proposes a new method to define clusters of patients which takes advantage of the power of association rules method. We present different notions of association and we specify the notion of frequent almost closed itemset which is the most appropriate for applications in the medical area. Applied to Hodgkin's disease to help establish prognostic groups, the first results bring out some parameters for which classical statistic methods confirm that they are interesting.