Journal
ADVANCES IN COMPUTATIONAL INTELLIGENCE
Volume 509, Issue -, Pages 123-132Publisher
SPRINGER-VERLAG SINGAPORE PTE LTD
DOI: 10.1007/978-981-10-2525-9_12
Keywords
K-means; GA; Precomputation; DE heuristic
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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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