This paper describes a new topological map dedicated to clustering under probabilistic constraints. In general, traditional clustering is used in an unsupervised manner. However, in some cases, background information about the problem domain is available or imposed in the form of constraints in addition to data instances. In this context, we modify the popular GTM algorithm to take these "soft" constraints into account during the construction of the topology. We present experiments on synthetic known databases with artificial generated constraints for comparison with both GTM and another constrained clustering methods. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Benabdeslem, K., & Snoussi, J. (2009). A probabilistic approach for constrained clustering with topological map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5632 LNAI, pp. 413–426). https://doi.org/10.1007/978-3-642-03070-3_31
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