Empirical comparison of fast partitioning-based clustering algorithms for large data sets

Abstract

Several fast algorithms for clustering very large data sets have been proposed in the literature, including CLARA, CLARANS, GAC-R3, and GAC-RARw. 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-R3 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 described by data size, number of clusters, cluster distinctness, cluster asymmetry, and data randomness. According to the experimental results, CLARANS outperforms its counterparts both in clustering quality and execution time when the number of clusters increases, clusters are more closely related, more asymmetric clusters are present, or more random objects exist in the data set. With a specific number of clusters, CLARA can efficiently achieve satisfactory clustering quality when the data size is larger, whereas GAC-R3 and GAC-RARw can achieve satisfactory clustering quality and efficiency when the data size is small, the number of clusters is small, and clusters are more distinct and symmetric.