Harmony Search Optimization in K-Means Clustering
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2013, Vol 5, Issue 6
Abstract
Clustering is a data mining technique that classifies a set of observations into clusters based on some similarity measures. The most commonly used partitioning based clustering algorithm is K-means. However, the K-means algorithm has several drawbacks. The algorithm generates a local optimal solution based on the randomly chosen initial centroids. Harmony Search is a recently developed meta-heuristic optimization algorithm which helps to find out near global optimal solutions by searching the entire solution space. The hybrid algorithm that combines harmony search and K-means produce a better solution.
Authors and Affiliations
Samina Ahamed K T , Jasila E K
Keywords
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