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| Cluster Validity Indexes for FCM Clustering Algorithm |
| PIAO Shang-Zhe1,2, Chaomurilige1, YU Jian1 |
1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044.
2.Digital Library, Kim II Sung University, PyongYang, Democratic People′s Republic of Korea |
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Abstract The clustering quality of fuzzy C-means (FCM) clustering algorithm is affected by several factors, such as initial setting of cluster centroid, the number of clusters and fuzzy index. In this paper, a comparative study on recently published five clustering validity measurement in different application fields is presented, e.g., different dimension of data, different cluster number and different fuzzy index. The experimental results show that the validity index based on ratio of within-class compactness and between-class separation is robust to data dimension and noise, and the validity index based on degree of membership can be applied to dataset with low dimension. The research results provide researchers with an option of selecting a suitable fuzzy clustering validity index for different application environments.
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Received: 25 March 2014
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2015, Vol. 28 
