The ability to effectively organize retrieval results becomes more important as the focus of Information Retrieval (IR) shifts towards interactive search processes. Automatic classification techniques are capable of providing the necessary information organization by arranging the retrieved data into groups of documents with common subjects. In this paper, we compare classification methods from IR and Machine Learning (ML) for clustering search results. Issues such as document representation, classification algorithms, and cluster representation are discussed. We introduce several evaluation techniques and use them in preliminary experiments. These experiments indicate that the proposed techniques have promise, but it is clear that user experiments are required to carry out more thorough evaluation.

Abstract: The proposed divisive clustering method performs simultaneously a hierarchy of a set of objects and a monothetic characterization of each cluster of the hierarchy. A division is performed according to the within-cluster inertia criterion which is minimized among the bipartitions induced by a set of binary questions. In order to improve the clustering, the algorithm revises at each step the division which has induced the cluster chosen for division.

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