Constrained clustering by constraint programming

Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often...

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Veröffentlicht in:Artificial intelligence Jg. 244; H. 244; S. 70 - 94
Hauptverfasser: Dao, Thi-Bich-Hanh, Duong, Khanh-Chuong, Vrain, Christel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.03.2017
Elsevier Science Ltd
Elsevier
Schlagworte:
Artificial Intelligence
Bi-criterion clustering
Clustering
Clusters
Coloring
Computer Science
Constrained clustering
Constraint programming
Constraints
Criteria
Filtering algorithm
Filtration
Global optimization
Global optimization constraint
Graph coloring
Modeling
Partitions
Programming
Theory of constraints
Filtering algorithm
Global optimization constraint
Bi-criterion clustering
Modeling
Constrained clustering
Constraint programming
ISSN:0004-3702, 1872-7921
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Zusammenfassung:Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2015.05.006