SOM based clustering with instance-level constraints

This paper describes a new topological map dedicated to clustering under instance-level 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 SOM algorithm to take these constraints into account during the construction of the topology. We present experiments on synthetic known databases with artificial constraints. We then apply the new method to a real problem of clustering melanoma data in health domain.