• 1987 (Vol.1)

General ensemble method for multi-view clustering

© 2021 I. I. Baikov, E. A. Semerova, A. I. Kurmukov

Higher School of Economics – National Research University, 101000 Moscow, Myasnitskaya ulitsa, 20, Russian
Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), 127994 Moscow, Bolshoy Karetny pereulok, 19, Russian

Received 02 Nov 2020

Network community detection is a task of dividing a set of network’s nodes into groups, such that intra-group connections are more dense than inter-group connections. We consider a specific type of clustering, so-called multi-view clustering, which deal with a set of graphs defined on the same set of nodes, but different edges. The goal is to divide nodes into subgroups taking into account all graphs. We propose an ensemble method for multi-view clustering, applicable to any greedy algorithm with nodes traversal. Instead of traversing nodes of the graphs individually, our approach does a co-clustering of all networks’ nodes. Decision about node color takes into account connections in all input graphs. We demonstrate the performance of our method, applied on a popular Louvain modularity algorithm, using real dataset and a synthetic dataset (with known clustering structure).

Key words: community detection, multi-view clustering, ensemble methods, Louvain modularity

DOI: 10.31857/S0235009221010029

Cite: Baikov I. I., Semerova E. A., Kurmukov A. I. Metod ansamblirovaniya algoritmov klasterizatsii dlya resheniya zadachi sovmestnoi klasterizatsii [General ensemble method for multi-view clustering]. Sensornye sistemy [Sensory systems]. 2021. V. 35(1). P. 43–49 (in Russian). doi: 10.31857/S0235009221010029


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