Frequent Closed Patterns Based Multiple Consensus Clustering

Abstract : Clustering is one of the major tasks in data mining. However, selecting an algorithm to cluster a dataset is a difficult task, especially if there is no prior knowledge on the structure of the data. Consensus clustering methods can be used to combine multiple base clusterings into a new solution that provides better partitioning. In this work, we present a new consensus clustering method based on detecting clustering patterns by mining frequent closed itemset. Instead of generating one consensus, this method both 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.
Type de document :
Communication dans un congrès
International Conference on Artificial Intelligence and Soft Computing, Jun 2016, Zakopane, Poland. Springer International Publishing, Proceedings of the ICAISC'2016 International Conference, Part II, Lecture Notes in Computer Science 9693, pp.14-26, 2016, <http://www.icaisc.eu/>. <10.1007/978-3-319-39384-1_2>


https://hal.archives-ouvertes.fr/hal-01329545
Contributeur : Nicolas Pasquier <>
Soumis le : jeudi 9 juin 2016 - 13:02:07
Dernière modification le : vendredi 26 août 2016 - 10:53:36

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Atheer Al-Najdi, Nicolas Pasquier, Frédéric Precioso. Frequent Closed Patterns Based Multiple Consensus Clustering. International Conference on Artificial Intelligence and Soft Computing, Jun 2016, Zakopane, Poland. Springer International Publishing, Proceedings of the ICAISC'2016 International Conference, Part II, Lecture Notes in Computer Science 9693, pp.14-26, 2016, <http://www.icaisc.eu/>. <10.1007/978-3-319-39384-1_2>. <hal-01329545>

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