Data clustering is a major, but a hard, task in the unsupervised learning domain. This process is used in various context such as Knowledge Discovery, representation or description simplification of a data set.In this study, we present the clustering algorithm PoBOC which organizes a dataset into overlapping classes which naturally match with real concepts of data. This clustering method is used in two very different applications.- In the supervised learning field, the induction of a set of propositional and first-order rules is performed by first organizing each class into sub-classes.- In the Information Retrieval field, the ambiguities from natural langage naturally induce overlaps between thematic.On these two research domains, the organization of a dataset into overlapping clusters is validated with suitable experimental studies.
