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Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
An experimental study of concept formation
An experimental study of concept formation
Clustering Binary Fingerprint Vectors with Missing Values for DNA Array Data Analysis
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Legal Theory, Sources of Law and the Semantic Web
Proceedings of the 2009 conference on Legal Theory, Sources of Law and the Semantic Web
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The concept of stereotype seems to be really adapted when wishing to extract meaningful descriptions from data, especially when there is a high rate of missing values. This paper proposes a logical framework called default clustering based on default reasoning and local search techniques. The first experiment deals with the rediscovering of initial descriptions from artificial data sets, the second one extracts stereotypes of politicians in a real case generated from newspaper articles. It is shown that default clustering is more adapted in this context than the three classical clusterers considered.