Recent advances in conceptual clustering: CLUSTER3 Conceptual clustering is a form of unsupervised learning that seeks clusters in data that represent simple and understandable concepts, rather than groupings of entities with high intra-cluster and low inter-cluster similarity, as conventional clustering. Another difference from conventional clustering is that conceptual clustering produces not only clusters but also their generalized descriptions, and that the descriptions are used for cluster evaluation, interpretation, and classification of new, previously unseen entities. Basic methodology of conceptual clustering and program CLUSTER3 implementing recent advances are briefly described. One important novelty in CLUSTER3 is the ability to generate clusters according to the $viewpoint$ from which clustering is to be performed. This is achieved through the view-relevant attribute subsetting method. CLUSTER3’s performance is illustrated by its application to clustering a database of automobile fatality accidents.

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