$k$ -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by $k$ -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap, Deterministic Random Swap, Repeated k-means or k-means++. The algorithm is successfully applied to document clustering and color image quantization as well." /> $k$ -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by $k$ -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap, Deterministic Random Swap, Repeated k-means or k-means++. The algorithm is successfully applied to document clustering and color image quantization as well." /> $k$ -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by $k$ -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap, Deterministic Random Swap, Repeated k-means or k-means++. The algorithm is successfully applied to document clustering and color image quantization as well." /> Centroid Ratio for a Pairwise Random Swap Clustering Algorithm
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Issue No.05 - May (2014 vol.26)
pp: 1
Pasi Franti , School of Computing, University of Eastern Finland, Joensuu, Finland
ABSTRACT
Clustering algorithm and cluster validity are two highly correlated parts in cluster analysis. In this paper, a novel idea for cluster validity and a clustering algorithm based on the validity index are introduced. A Centroid Ratio is firstly introduced to compare two clustering results. This centroid ratio is then used in prototype-based clustering by introducing a Pairwise Random Swap clustering algorithm to avoid the local optimum problem of $k$ -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by $k$ -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap, Deterministic Random Swap, Repeated k-means or k-means++. The algorithm is successfully applied to document clustering and color image quantization as well.
INDEX TERMS
textual and multimedia data, Algorithms, Similarity measures, Quantization, Modeling structured,
CITATION
Pasi Franti, "Centroid Ratio for a Pairwise Random Swap Clustering Algorithm", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 5, pp. 1, May 2014, doi:10.1109/TKDE.2013.113
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