A Framework for Actionable Clustering using Constraint Programming

Abstract : Consider if you wish to cluster your ego network in Facebook so as to find several useful groups each of which you can invite to a different dinner party. You may require that each cluster must contain equal number of males and females, that the width of a cluster in terms of age is at most 10 and that each person in a cluster should have at least $r$ other people with the same hobby. These are examples of cardinality, geometric and density requirements/constraints respectfully that can make the clustering useful for a given purpose. However existing formulations of constrained clustering were not designed to handle these constraints since they typically deal with low-level, instance-level constraints. We formulate a constraint programming (CP) languages formulation of clustering with these cluster-level styles of constraints which we call \emph{actionable clustering}. Experimental results show the potential uses of this work to make clustering more actionable. We also show that these constraints can be used to improve the accuracy of semi-supervised clustering.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01341954
Contributor : Thi-Bich-Hanh Dao <>
Submitted on : Tuesday, July 5, 2016 - 10:52:55 AM
Last modification on : Wednesday, August 7, 2019 - 12:19:20 PM

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  • HAL Id : hal-01341954, version 1

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Thi-Bich-Hanh Dao, Christel Vrain, Khanh-Chuong Duong, Ian Davidson. A Framework for Actionable Clustering using Constraint Programming. 22nd European Conference on Artificial Intelligence, Aug 2016, The Hague, Netherlands. ⟨hal-01341954⟩

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