Spontaneous Clustering via Minimum Gamma-Divergence

We propose a new method for clustering based on local minimization of the gamma-divergence, which we call spontaneous clustering. The greatest advantage of the proposed method is that it automatically detects the number of clusters that adequately reflect the data structure. In contrast, existing me...

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Bibliographic Details
Published inNeural computation Vol. 26; no. 2; pp. 421 - 448
Main Authors Notsu, Akifumi, Komori, Osamu, Eguchi, Shinto
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.02.2014
MIT Press Journals, The
Subjects
Cluster Analysis
Clustering
Clusters
Comparative analysis
Computer simulation
Fuzzy
Fuzzy Logic
Fuzzy set theory
Letters
Neurosciences
Numerical analysis
Optimization
Pattern Recognition, Automated - methods
Simulation
Spontaneous
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Summary:We propose a new method for clustering based on local minimization of the gamma-divergence, which we call spontaneous clustering. The greatest advantage of the proposed method is that it automatically detects the number of clusters that adequately reflect the data structure. In contrast, existing methods, such as -means, fuzzy -means, or model-based clustering need to prescribe the number of clusters. We detect all the local minimum points of the gamma-divergence, by which we define the cluster centers. A necessary and sufficient condition for the gamma-divergence to have local minimum points is also derived in a simple setting. Applications to simulated and real data are presented to compare the proposed method with existing ones.
Bibliography:February, 2014
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ISSN:0899-7667
1530-888X
DOI:10.1162/NECO_a_00547