An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Abstract

We propose a parallel graph-based data clustering algorithm using CUDA GPU, based on exact clustering of the minimum spanning tree in terms of a minimum isoperimetric criteria. We also provide a comparative performance analysis of our algorithm with other related ones which demonstrates the general superiority of this parallel algorithm over other competing algorithms in terms of accuracy and speed.

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Cite this paper

@article{Javadi2017AnEP, title={An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees}, author={Ramin Javadi and Saleh Ashkboos}, journal={CoRR}, year={2017}, volume={abs/1702.04739} }