Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data | Zendy

Yukihiro Hamasuna | Zendy; Shusuke Nakano | Zendy; Ryo Ozaki | Zendy; and Yasunori Endo | Zendy

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Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data

Author(s) -

Yukihiro Hamasuna,

Shusuke Nakano,

Ryo Ozaki,

and Yasunori Endo

Publication year - 2019

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2019.p0577

Subject(s) - hierarchical clustering , modularity (biology) , cluster analysis , computer science , single linkage clustering , hierarchical clustering of networks , data mining , cluster (spacecraft) , measure (data warehouse) , hierarchical network model , complete linkage clustering , brown clustering , artificial intelligence , pattern recognition (psychology) , fuzzy clustering , cure data clustering algorithm , programming language , biology , genetics

The Louvain method is a method of agglomerative hierarchical clustering (AHC) that uses modularity as the merging criterion. Modularity is an evaluation measure for network partitions. Cluster validity measures are also used to evaluate cluster partitions and to determine the optimal number of clusters. Several cluster validity measures are constructed considering the geometric features of clusters. These measures and modularity are considered to be the same concept in the viewpoint of evaluating cluster partitions. In this paper, cluster validity measures based agglomerative hierarchical clustering (CVAHC) is proposed as a novel clustering method for network data. The cluster validity measures are used as a merging criterion and an evaluation measure for network data in the proposed method. Numerical experiments show that Dunn’s and Xie-Beni’s indices for network partitions are useful for network clustering.

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