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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  • EP ID EP162193
  • DOI -
  • Views 106
  • Downloads 0

How To Cite

Samina Ahamed K T, Jasila E K (2013). Harmony Search Optimization in K-Means Clustering. INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY, 5(6), 287-291. https://www.europub.co.uk/articles/-A-162193