An efficient parallel direction-based clustering algorithm.

Clustering, which explores the visualization and distribution of data, has recently been studied widely. Although the existing clustering algorithms can well detect arbitrary shape clusters, most of them face the limitation that they cluster points on the basis of two physical metrics, distance and density, but ignore the orientation relationship of data distribution. Beside, they have a difficulty of selecting suitable parameters, which are important inputs of the clustering algorithms. In this paper, we firstly introduce a new physical metric, namely direction. Then, based on this new metric, we propose an adaptive direction-based clustering algorithm, namely ADC, which can automatically calculate appropriate parameters. Finally, we develop a parallel ADC algorithm based on multi-processors to improve the performance of the ADC algorithm. Compared with other clustering algorithms, experimental results demonstrate that the proposed algorithms are more general and can get much better clustering results. In addition, the parallel ADC algorithm has the best scalability over large data sets. • A new physical metric, direction, has been considered in the proposed clustering algorithm. • Adaptive strategies can obtain the parameter automatically. • A parallel algorithm PADC is proposed based on multi-core. • The experimental results demonstrate that the proposed PADC algorithm can efficiently process very large scale data sets with a good performance. [ABSTRACT FROM AUTHOR]