Multiple Consensuses Clustering by Iterative Merging/Splitting of Clustering Patterns

Abstract : The existence of many clustering algorithms with variable performance on each dataset made the clustering task difficult. Consensus clustering tries to solve this problem by combining the partitions generated by different algorithms to build a new solution that is more stable and achieves better results. In this work, we propose a new consensus method that, unlike others, give more insight on the relations between the different partitions in the clusterings ensemble, by using the frequent closed itemsets technique, usually used for association rules discovery. Instead of generating one consensus, our method generates multiple consensuses based on varying the number of base clusterings, and links these solutions in a hierarchical representation that eases the selection of the best clustering. This hierarchical view also provides an analysis tool, for example to discover strong clusters or outlier instances.
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Communication dans un congrès
Machine Learning and Data Mining in Pattern Recognition, Jul 2016, New York, United States. Springer International Publishing, Proceedings of the MLDM'2016 International Conference, Lecture Notes in Artificial Intelligence 9729, pp.790-804, <http://www.mldm.de/>. <10.1007/978-3-319-41920-6>
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https://hal.archives-ouvertes.fr/hal-01330216
Contributeur : Nicolas Pasquier <>
Soumis le : vendredi 10 juin 2016 - 11:31:31
Dernière modification le : vendredi 26 août 2016 - 10:54:45

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Atheer Al-Najdi, Nicolas Pasquier, Frédéric Precioso. Multiple Consensuses Clustering by Iterative Merging/Splitting of Clustering Patterns. Machine Learning and Data Mining in Pattern Recognition, Jul 2016, New York, United States. Springer International Publishing, Proceedings of the MLDM'2016 International Conference, Lecture Notes in Artificial Intelligence 9729, pp.790-804, <http://www.mldm.de/>. <10.1007/978-3-319-41920-6>. <hal-01330216>

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