Abstract
Clustering has been used extensively for exploratory data analysis. GK clustering algorithm can provide a data partition that is more meaningful than the standard fuzzy c-means and its variants. In this paper we propose a novel approach towards fuzzy clustering which reduces the processing time significantly while keeping the results highly accurate. It is a matrix based approach using the concept of equivalent samples and the weighting samples. Equivalence is measured in terms of proximity of the samples and then weighted samples are used as an input to the modified GK clustering algorithm. Objective function and validation index estimates are used to assess the goodness of partition. Experimental results are shown to emphasize the benefits of the proposed technique in domains like Telecom where we have massive data sets to be processed for real time clustering and recommendation engines.
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Aggarwal, G., Bhatia, M.P.S. (2012). A Novel Faster Approximate Fuzzy Clustering Approach with Highly Accurate Results. In: Parashar, M., Kaushik, D., Rana, O.F., Samtaney, R., Yang, Y., Zomaya, A. (eds) Contemporary Computing. IC3 2012. Communications in Computer and Information Science, vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32129-0_25
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DOI: https://doi.org/10.1007/978-3-642-32129-0_25
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