An Improved Heuristic K-Means Clustering Method Using Genetic Algorithm Based Initialization

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 509)

Abstract

In this paper, we propose methods to remove the drawbacks that commonly afflict the k-means clustering algorithm. We use nature based heuristics to improve the clustering performance offered by the k-means algorithm and also ensure the creation of the requisite number of clusters. The use of GA is found to be adequate in this case to provide a good initialization to the algorithm, and this is followed by a differential evolution based heuristic to ensure that the requisite number of clusters is created without minimal increase in the running time of the algorithm.

Keywords

K-means GA Precomputation DE heuristic 
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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of CSEBirla Institute of TechnologyMesraIndia

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