Topology-Based Clustering Using Polar Self-Organizing Map

被引：6

作者：

Xu, Lu ^{[1
]}

Chow, Tommy W. S. ^{[1
]}

Ma, Eden W. M. ^{[2
]}

机构：

[1] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China

[2] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2015年 / 26卷 / 04期

关键词：

Clustering; polar self-organizing map (PolSOM); unsupervised learning; visualization; NETWORKS;

D O I：

10.1109/TNNLS.2014.2326427

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cluster analysis of unlabeled data sets has been recognized as a key research topic in varieties of fields. In many practical cases, no a priori knowledge is specified, for example, the number of clusters is unknown. In this paper, grid clustering based on the polar self-organizing map (PolSOM) is developed to automatically identify the optimal number of partitions. The data topology consisting of both the distance and density is exploited in the grid clustering. The proposed clustering method also provides a visual representation as PolSOM allows the characteristics of clusters to be presented as a 2-D polar map in terms of the data feature and value. Experimental studies on synthetic and real data sets demonstrate that the proposed algorithm provides higher clustering accuracy and lower computational cost compared with six conventional methods.

引用

页码：798 / 808

页数：11

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