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.
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Mustafi, D., Sahoo, G., Mustafi, A. (2017). An Improved Heuristic K-Means Clustering Method Using Genetic Algorithm Based Initialization. In: Sahana, S.K., Saha, S.K. (eds) Advances in Computational Intelligence. ICCI 2015. Advances in Intelligent Systems and Computing, vol 509. Springer, Singapore. https://doi.org/10.1007/978-981-10-2525-9_12
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DOI: https://doi.org/10.1007/978-981-10-2525-9_12
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