Using Frequent Closed Pattern Mining to Solve a Consensus Clustering Problem

Abstract : Clustering is the process of partitioning a dataset into groups based on the similarity between the instances. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus solution. In this work, we propose a new consensus method that uses a pattern mining technique in order to reduce the search space from instance-based into pattern-based space. Instead of finding one solution, our method generates multiple consensus candidates based on varying the number of base clusterings considered. The different solutions are then linked and presented as a tree that gives more insight about the similarities between the instances and the different partitions in the ensemble.
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Communication dans un congrès
KSI Research Inc. International Conference on Software Engineering & Knowledge Engineering, Jul 2016, Redwood City, United States. pp.454-461, Proceedings of the SEKE'2016 International Conference. SEKE'2016 Third Place Award. <http://ksiresearchorg.ipage.com/seke/seke16.html>. <10.18293/SEKE2016-117>
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https://hal.archives-ouvertes.fr/hal-01330079
Contributeur : Nicolas Pasquier <>
Soumis le : jeudi 9 juin 2016 - 21:54:12
Dernière modification le : vendredi 26 août 2016 - 10:55:40

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Atheer Al-Najdi, Nicolas Pasquier, Frédéric Precioso. Using Frequent Closed Pattern Mining to Solve a Consensus Clustering Problem. KSI Research Inc. International Conference on Software Engineering & Knowledge Engineering, Jul 2016, Redwood City, United States. pp.454-461, Proceedings of the SEKE'2016 International Conference. SEKE'2016 Third Place Award. <http://ksiresearchorg.ipage.com/seke/seke16.html>. <10.18293/SEKE2016-117>. <hal-01330079>

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