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Erschienen in:
24.04.2019
Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering
Erschienen in: Journal of Classification | Ausgabe 2/2020
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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.