Frequent Closed Patterns Based Multiple Consensus Clustering - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

Frequent Closed Patterns Based Multiple Consensus Clustering


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.
No file

Dates and versions

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



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⟩
133 View
0 Download



Gmail Facebook Twitter LinkedIn More