期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 20, 期 11, 页码 1519-1534出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2008.88
关键词
fuzzy K-Means clustering; agglomerative; number of clusters; cluster validation
类别
资金
- Council of Hong Kong SAR [7045/04P, 7045/05P, HKBU 2156/04E, HKBU 210306]
- Hong Kong Baptist University [HKBU 05-06/II-42]
- Natural Science Foundation of China [60603066]
In this paper, we present an agglomerative fuzzy K-Means clustering algorithm for numerical data, an extension to the standard fuzzy K-Means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters in a data set, which is a well-known problem in K-Means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5,000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20,000 objects and 100 cluster0and the WINE data set of 178 objects, 17 dimensions, and 3 clusters from UCI have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters.
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