Frequent Closed Patterns Based Multiple Consensus Clustering - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Frequent Closed Patterns Based Multiple Consensus Clustering

Résumé

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.
Fichier non déposé

Dates et versions

hal-01329545 , version 1 (09-06-2016)

Identifiants

Citer

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. pp.14-26, ⟨10.1007/978-3-319-39384-1_2⟩. ⟨hal-01329545⟩
137 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More