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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

Tianshu Yang 1, 2 Nicolas Pasquier 1 Antoine Hom 2 Laurent Dollé 2 Frédéric Precioso 1, 3
3 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems, UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné
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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https://hal.archives-ouvertes.fr/hal-02917863
Contributor : Nicolas Pasquier <>
Submitted on : Thursday, August 20, 2020 - 1:22:52 AM
Last modification on : Monday, July 12, 2021 - 3:09:56 PM

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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, Oct 2020, Galway, Ireland. pp.3341-3344, ⟨10.1145/3340531.3417453⟩. ⟨hal-02917863⟩

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