Abstract
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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© 1986 Springer-Verlag Berlin Heidelberg
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Puget, J.F., Benamou, N., Vrain, C., Kodratoff, Y. (1986). Hierarchical Conceptual Clustering. In: Sriram, D., Adey, R. (eds) Applications of Artificial Intelligence in Engineering Problems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-21626-2_5
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DOI: https://doi.org/10.1007/978-3-662-21626-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-21628-6
Online ISBN: 978-3-662-21626-2
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