# An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

## TL;DR

This paper introduces a CUDA GPU-based parallel clustering algorithm that leverages the isoperimetric number of trees for efficient and accurate data clustering, outperforming existing methods in speed and accuracy.

## Contribution

The paper presents a novel parallel clustering algorithm using the isoperimetric number of trees, optimized for GPU execution, with comprehensive performance comparison.

## Key findings

- Superior accuracy compared to related algorithms
- Faster clustering performance on GPU hardware
- Effective use of isoperimetric criteria for clustering

## 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.