Hierarchical Conceptual Clustering
In this paper the following problem is studied : how a hierarchical conceptual classification of a given set of examples can be discovered ?
Traditional techniques for this purpose, developed in numerical data analysis, are often inadequate because they cluster objects solely on the basis of a numerical measure of similarity. Thus the clusters obtained have no simple descriptions. This limitation is overcome by the conceptual hierarchical methods shown here .
Firstly, the algorithm CLUSTER/2 is introduced. We describe the representation language used, the basic routines, and show how it works on an example.
Secondly, we are developing a conceptual hierarchy building method based on the similarity between events or event sets using the notion of Similarity vectors. The algorithm outputs several equally sensible hierarchies.
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