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標題: A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM
作者: Shieh, S.L.
Liao, I.E.
關鍵字: self-organizing map;clustering;clustering validity index;self-organizing map;neural networks;projection
Project: Ieice Transactions on Information and Systems
期刊/報告no:: Ieice Transactions on Information and Systems, Volume E92D, Issue 9, Page(s) 1668-1674.
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.
ISSN: 0916-8532
DOI: 10.1587/transinf.E92.D.1668
Appears in Collections:資訊科學與工程學系所

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