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Selection of 'K' in K-means clustering using GA and VMA

Author

Listed:
  • Sanjay Chakraborty
  • Subham Raj
  • Shreya Garg

Abstract

The K-means algorithm is the most widely used partitional clustering algorithms. In spite of several advances in K-means clustering algorithm, it suffers in some drawbacks like initial cluster centres, stuck in local optima etc. The initial guessing of cluster centres lead to the bad clustering results in K-means and this is one of the major drawbacks of K-means algorithm. In this paper, a new strategy is proposed where we have blended K-means algorithm with genetic algorithm (GA) and volume metric algorithm (VMA) to predict the best value of initial cluster centres, which is not in the case of only K-means algorithm. The paper concludes with the analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different well-known datasets from UCI machine learning repository.

Suggested Citation

  • Sanjay Chakraborty & Subham Raj & Shreya Garg, 2019. "Selection of 'K' in K-means clustering using GA and VMA," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 4(1), pages 63-81.
    • Handle: RePEc:ids:ijdsci:v:4:y:2019:i:1:p:63-81
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