Abstract
The Clique Partitioning Problem constitutes a general framework for clustering, when similarities assume positive and negative values. The effectiveness of various heuristic methods of solving the above problem has been proved by several authors.
In order to evaluate the validity of one optimal classification P* obtained by this approach, we propose a hierarchical process, working on the clusters of P*. Thus, we obtain a family of classifications, and the concept of pertinence enables us to detect the interesting classifications.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Nicoloyannis, N., Terrenoire, M., Tounissoux, D. (1998). Pertinence for a Classification. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_24
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DOI: https://doi.org/10.1007/978-3-642-72253-0_24
Publisher Name: Springer, Berlin, Heidelberg
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