Empirical Comparison of Fast Clustering Algorithms for Large Data Sets

@inproceedings{Wei2000EmpiricalCO,
  title={Empirical Comparison of Fast Clustering Algorithms for Large Data Sets},
  author={Chih-Ping Wei and Yen-Hsien Lee and Che-Ming Hsu},
  booktitle={HICSS},
  year={2000}
}
Several fast algorithms for clustering very large data sets have been proposed in the literature. CLARA is a combination of a sampling procedure and the classical PAM algorithm, while CLARANS adopts a serial randomized search strategy to find the optimal set of medoids. GAC-R and GAC-RARw exploit genetic search heuristics for solving clustering problems. In this research, we conducted an empirical comparison of these four clustering algorithms over a wide range of data characteristics… CONTINUE READING
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