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| Improved Fuzzy Clustering Algorithm Based on Data Weighted Approach |
| Tang Cheng-long; Wang Shi-gang; Xu Wei |
| Institute of Mechanical-Electrical Design and Knowledge Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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Abstract A new data exponent weighted fuzzy clustering approach is proposed by introducing a set of exponent weighting factors and influence exponent, the new approach makes it possible to treat the data points discriminatively. The new approach is combined with the existing Gustafson-Kessel (G-K) algorithm and a new algorithm, DWG-K is presented. Numerical experiments show that the DWG-K is better than G-K in improving the quality of clustering, and in the outliers mining, DWG-K detects the outliers with the global view and the physical meaning of outliers is clearer, and moreover, the computational efficiency is significantly higher than the current widely used density-based method.
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Received: 05 June 2009
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Corresponding Authors:
Wang Shi-gang
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2010, Vol. 32 
