Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Randomized algorithms
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Performance guarantees for hierarchical clustering
Journal of Computer and System Sciences - Special issue on COLT 2002
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Document clustering using synthetic cluster prototypes
Data & Knowledge Engineering
Feature weighted unsupervised classification algorithm and adaptation for software cost estimation
International Journal of Computational Intelligence Studies
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Although each iteration of the popular k-Means clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to a solution. This paper introduces an enhancement of k-Means in which local search is used to accelerate convergence without greatly increasing the average computational cost of the iterations. The local search involves a carefully-controlled number of swap operations resembling those of the more robust k-Medoids clustering heuristic. We show empirically that the proposed method improves convergence results when compared to standard k-Means.