Abstract
In this paper, we present a novel evolutionary fuzzy clustering approach with Minkowski distances. Fuzzy clustering plays an important role for various kinds of classification problems. Evolutionary algorithm is used for searching the best partitioning among the populations generated by different runs of the fuzzy clustering algorithm. Evolutionary fuzzy clustering performs better as compared to the conventional fuzzy clustering in terms of classification accuracy and partitioning. Fuzzy c-means (FCM) is a data clustering algorithm in which each data point is associated with a cluster through a membership degree. Here, Minkowski distance is used with FCM instead of conventional Euclidian distance because of its more generalized nature. It does not restrict the shape of the clusters generated. Empirical evaluation demonstrates the performance of proposed novel technique in terms of precision and accuracy in various benchmark problems.
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Srivastava, V., Tripathi, B.K., Pathak, V.K. (2011). An Evolutionary Fuzzy Clustering with Minkowski Distances. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_87
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DOI: https://doi.org/10.1007/978-3-642-24958-7_87
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