A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM

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概要

• 論文の詳細を見る

• Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.

• （社）電子情報通信学会の論文
• 2009-09-01

著者

• SHIEH Shu-Ling

  Department of Computer Science and Engineering, National Chung-Hsing University

• Shieh Shu-ling

  Department Of Computer Science And Engineering National Chung-hsing University

• Liao I-en

  Department Of Computer Science And Engineering National Chung-hsing University

関連論文

• An Efficient Initialization Scheme for SOM Algorithm Based on Reference Point and Filters
• A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM

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