Parallel Spectral Clustering Algorithm Based on Hadoop

TL;DR

This paper presents a parallel spectral clustering algorithm implemented on Hadoop, leveraging cloud computing to improve clustering efficiency and accuracy, with experimental validation demonstrating its effectiveness.

Contribution

It introduces a novel parallel spectral clustering method based on Hadoop, addressing scalability and performance issues of traditional spectral clustering algorithms.

Findings

The algorithm achieves improved scalability on large datasets.

Experimental results show enhanced clustering accuracy.

The method demonstrates efficient parallel processing capabilities.

Abstract

Spectral clustering and cloud computing is emerging branch of computer science or related discipline. It overcome the shortcomings of some traditional clustering algorithm and guarantee the convergence to the optimal solution, thus have to the widespread attention. This article first introduced the parallel spectral clustering algorithm research background and significance, and then to Hadoop the cloud computing Framework has carried on the detailed introduction, then has carried on the related to spectral clustering is introduced, then introduces the spectral clustering arithmetic Method of parallel and relevant steps, finally made the related experiments, and the experiment are summarized.