Semi-supervised Consensus Clustering Based on Frequent Closed Itemsets - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Semi-supervised Consensus Clustering Based on Frequent Closed Itemsets

Résumé

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.
Fichier non déposé

Dates et versions

hal-02917863 , version 1 (20-08-2020)

Identifiants

Citer

Tianshu Yang, Nicolas Pasquier, Antoine Hom, Laurent Dollé, Frédéric Precioso. Semi-supervised Consensus Clustering Based on Frequent Closed Itemsets: Amadeus Intellectual Property Invention Patent ID2326WW00 "Clustering Techniques for Revenue Accounting Error-Handling Automation" Defensive Paper. CIKM'2020 29th ACM International Conference on Information and Knowledge Management (Acceptance Rate: 18%), Oct 2020, Galway, Ireland. pp.3341-3344, ⟨10.1145/3340531.3417453⟩. ⟨hal-02917863⟩
136 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More