Abstract
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.
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Benabdeslem, K., Snoussi, J. (2009). A Probabilistic Approach for Constrained Clustering with Topological Map. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_31
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DOI: https://doi.org/10.1007/978-3-642-03070-3_31
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