A Filtering Algorithm for Constrained Clustering with Within-Cluster Sum of Dissimilarities Criterion

Abstract : Constrained clustering is an important task in Data Mining. In the last ten years, many works have been done to extend classical clustering algorithms to handle user-defined constraints, but restricted to handle one kind of user-constraints. In a previous work \cite{ecml2013}, we have proposed a declarative and generic framework, based on Constraint Programming, which enables to design a clustering task by specifying an optimization criterion and different kinds of user-constraints. One of the criteria is the within-cluster sum of dissimilarities, which is represented by a sum constraint and reified equality constraints. A direct implementation using predefined constraints is not effective as the propagation of theses constraints is weak. In this paper, we consider this criterion as a global constraint and develop a filtering algorithm for it. This filtering helps to improve significantly the model performance. Experiments on classical databases show the interest of our approach.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00870533
Contributor : Thi-Bich-Hanh Dao <>
Submitted on : Monday, October 7, 2013 - 2:56:32 PM
Last modification on : Thursday, January 17, 2019 - 3:06:04 PM

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Thi-Bich-Hanh Dao, Khanh-Chuong Duong, Christel Vrain. A Filtering Algorithm for Constrained Clustering with Within-Cluster Sum of Dissimilarities Criterion. IEEE International Conference on Tools with Artificial Intelligence (ICTAI) - 2013, Nov 2013, Washington DC, United States. ⟨hal-00870533⟩

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