An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees
Distributed, Parallel, and Cluster Computing
2017-02-17 v1
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.
Cite
@article{arxiv.1702.04739,
title = {An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees},
author = {Ramin Javadi and Saleh Ashkboos},
journal= {arXiv preprint arXiv:1702.04739},
year = {2017}
}
Comments
16 pages, 6 figures