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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Declarative Framework for Constrained Clustering
verfasst von : Thi-Bich-Hanh Dao, Khanh-Chuong Duong, Christel Vrain
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
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In recent years, clustering has been extended to constrained clustering, so as to integrate knowledge on objects or on clusters, but adding such constraints generally requires to develop new algorithms. We propose a declarative and generic framework, based on Constraint Programming, which enables to design clustering tasks by specifying an optimization criterion and some constraints either on the clusters or on pairs of objects. In our framework, several classical optimization criteria are considered and they can be coupled with different kinds of constraints. Relying on Constraint Programming has two main advantages: the declarativity, which enables to easily add new constraints and the ability to find an optimal solution satisfying all the constraints (when there exists one). On the other hand, computation time depends on the constraints and on their ability to reduce the domain of variables, thus avoiding an exhaustive search.