Semi-supervised consensus clustering based on frequent closed itemsets

19/10/2020

Semi-supervised consensus clustering based on frequent closed itemsets

Tianshu Yang, Nicolas Pasquier, Antoine Hom, Laurent Dolle, Frédéric Precioso

Keywords: semi-supervised learning, semi-supervised consensus clustering, frequent closed itemsets, clustering

Abstract Paper Similar Papers

Abstract: Semi-supervised consensus clustering integrates supervised information into consensus clustering in order to improve the quality of clustering. In this paper, we study the novel Semi-MultiCons semi-supervised consensus clustering method extending the previous MultiCons approach. Semi-MultiCons aims to improve the clustering result by integrating pairwise constraints in the consensus creation process and infer the number of clusters K using frequent closed itemsets extracted from the ensemble members. Experimental results show that the proposed method outperforms other state-of-art semi-supervised consensus algorithms.

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