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Wavelet Based Fuzzy Clustering of Higher Dimensional Data

C. Palanisamy, S. Selvan


In this paper, an approach for automatically clustering a data set into a number of fuzzy partitions with wavelets is proposed. This is an improved version of fuzzy clustering scheme in which fuzzy ant algorithm is utilized to give better initialization and thus the clustering performance. Appropriate subbands are selected and clustered. The effectiveness of the proposed technique is studied using various validity measures such as partition coefficient, classification entropy, separation index and Xie-Beni index. The experiments are carried out using both artificial and real-life data sets and it is found that our approach performs better than other approaches.


wavelets, fuzzy clustering, redundant wavelet transformation, validity measures

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