Characterization of unsupervised clusters with the simplest association rules: application for child's meningitis

We combine different recent data mining techniques to improve the symbolic description of unsupervised clusters. First, we use a clustering method that computes bi-partitions (a partition of examples and a related partition of attribute-value pairs). Then, we use an efficient association rule mining technique to describe the membership of examples within each cluster. We propose a technique for removing rules that are not relevant enough for the cluster characterization. An experimental validation on a real world medical data set is provided. Keywords. Conceptual clustering, association rule, characterization of clusters.