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Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering

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  • Mohammad Rezaei

    (University of Eastern Finland)

Abstract

Fast centroid-based clustering algorithms such as k-means usually converge to a local optimum. In this work, we propose a method for constructing a better clustering from two such suboptimal clustering solutions based on the fact that each suboptimal clustering has benefits regarding to including some of the correct clusters. We develop the new method COTCLUS to find two centroids from one clustering and replace them by two centroids from the other clustering so that the maximum decrease in the mean square error of the first clustering is achieved. After modifying centroids, k-means algorithm with few iterations is applied for fine-tuning. In an iterative algorithm, the resulting clustering is further improved using a new given clustering. The proposed method can find optimal clustering in a very small number of iterations for datasets with well-separated clusters. We demonstrate by experiments that the proposed method outperforms the selected competitive methods.

Suggested Citation

  • Mohammad Rezaei, 2020. "Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 352-365, July.
    • Handle: RePEc:spr:jclass:v:37:y:2020:i:2:d:10.1007_s00357-018-9296-4
    DOI: 10.1007/s00357-018-9296-4
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    References listed on IDEAS

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    1. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
    2. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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