Constrained Minimum Sum of Squares Clustering by Constraint Programming

Thi-Bich-Hanh Dao 1 Khanh-Chuong Duong 1 Christel Vrain 1
1 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
Abstract : The Within-Cluster Sum of Squares (WCSS) is the most used criterion in cluster analysis. Optimizing this criterion is proved to be NP-Hard and has been studied by different communities. On the other hand, Constrained Clustering allowing to integrate previous user knowledge in the clustering process has received much attention this last decade. As far as we know, there is a single approach that aims at finding the optimal solution for the WCSS criterion and that integrates different kinds of user constraints. This method is based on integer linear programming and column generation. In this paper, we propose a global optimization constraint for this criterion and develop a filtering algorithm. It is integrated in our Constraint Programming general and declarative framework for Constrained Clustering. Experiments on classic datasets show that our approach outperforms the exact approach based on integer linear programming and column generation.
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https://hal.archives-ouvertes.fr/hal-01168193
Contributeur : Thi-Bich-Hanh Dao <>
Soumis le : jeudi 25 juin 2015 - 13:50:11
Dernière modification le : jeudi 17 janvier 2019 - 15:06:06

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  • HAL Id : hal-01168193, version 1

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Thi-Bich-Hanh Dao, Khanh-Chuong Duong, Christel Vrain. Constrained Minimum Sum of Squares Clustering by Constraint Programming. 21st International Conference on Principles and Practice of Constraint Programming (CP 2015), Aug 2015, Cork, Ireland. ⟨hal-01168193⟩

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