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An Optimized K-Means Clustering Technique Using Bat Algorithm

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Executive Summary

Cluster analysis is one of the primary data analysis methods and K-Means algorithm is well known for its efficiency in clustering large data sets. In K-Means (KM) algorithm is one of the popular unsupervised learning clustering algorithms for cluster the large datasets but it is sensitive to the selection of initial cluster centroid, and selection of K value is an issue and sometimes it is hard to predict before the number of clusters that would be there in data. There are also no efficient and universal methods for the selection of K value, till now the authors selected a random value.

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