Novel Initialization Technique for K-Means Clustering Using Spectral Constraint Prototype

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Executive Summary

Clustering is a general technique used to classify collection of data into groups of related objects. One of the most commonly used clustering techniques in practice is K-Means clustering. The major limitation in K-Means is its initialization technique. Several attempts have been made by many researchers to solve this particular issue, but still there is no effective technique available for better initialization in K-Means. In general, K-Means follows randomly generated initial starting points which often result in poor clustering results. The better clustering results of K-Means technique can be accomplished after several iterations. However, it is very complicated to decide the computation limit for obtaining better results.

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