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Clustering Based on Yukawa Potential

Clustering is a common natural phenomenon. In microcosms, the nucleus is formed by the aggregation of nuclear particles through strong interactions, which can be illustrated by Yukawa potential. Inspired by this clustering phenomenon, we propose a novel dynamic clustering algorithm based on Yukawa potential (Yupc). Each data object is regarded as a particle following the basic rules of movements in the Yukawa potential field. After several time intervals, similar objects gradually aggregate together and form clear clusters. Yupc neither relies on any assumption of data distribution, nor prescribes any specific number of clusters. Natural clusters of different shapes, densities, sizes, numbers and distributions can be detected by Yupc, reflecting the intrinsic structure of the original data set. In addition, we propose a framework to automatically find appropriate parameters for Yupc. Experiments performed on synthetic and real-world data show that this approach outperforms existing algorithms, especially in data sets with arbitrary kinds of