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Preface
October
2007, 3(4): 619-624.
doi: 10.3934/jimo.2007.3.619
A new multimembership clustering method
1. | Department of Mathematics, West Virginia University, Morgantown, WV 26506-6310, United States, United States |
Clustering method is one of the most important tools in statistics.
In a graph theory model, clustering is the process of finding all
dense subgraphs. In this paper, a new clustering method is
introduced. One of the most significant differences between the new
method and other existing methods is that this new
method constructs
a much smaller hierarchical tree, which clearly highlights
meaningful clusters. Another important feature of the new method is
the feature of overlapping clustering or multi-membership. The
property of multi-membership is a concept that has recently received
increased attention in the literature (Palla, Derényi, Farkas and
Vicsek, (Nature 2005); Pereira-Leal, Enright and Ouzounis,
(Bioinformatics, 2004); Futschik and Carlisle, (J.
Bioinformatics and Computational Biology 2005))
Keywords:
overlap clustering,
Clustering,
dense subgraph.,
hierarchical
clustering,
multimembership clustering.
Mathematics Subject Classification:
90C26, 90C4.
Citation:
Yongbin Ou, Cun-Quan Zhang. A new multimembership clustering method. Journal of Industrial & Management Optimization,
2007, 3
(4)
: 619-624.
doi: 10.3934/jimo.2007.3.619
[1] |
Antonio Rieser. A topological approach to spectral clustering. Foundations of Data Science, 2021 doi: 10.3934/fods.2021005 |
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