Text document clustering using Spectral Clustering algorithm with Particle Swarm Optimization

doi:10.1016/j.eswa.2019.05.030

2019-11-01

117

PRE

Janani, R. *

Vijayarani, S.

DOI：10.1016/j.eswa.2019.05.030

原文链接

原文求助

摘要

En 中文

Document clustering is a gathering of textual content documents into groups or clusters. The main aim is to cluster the documents, which are internally logical but considerably different from each other. It is a crucial process used in information retrieval, information extraction and document organization. In recent years, the spectral clustering is widely applied in the field of machine learning as an innovative clustering technique. This research work proposes a novel Spectral Clustering algorithm with Particle Swarm Optimization (SCPSO) to improve the text document clustering. By considering global and local optimization function, the randomization is carried out with the initial population. This research work aims at combining the spectral clustering with swarm optimization to deal with the huge volume of text documents. The proposed algorithm SCPSO is examined with the benchmark database against the other existing approaches. The proposed algorithm SCPSO is compared with the Spherical K-means, Expectation Maximization Method (EM) and standard PSO Algorithm. The concluding results show that the proposed SCPSO algorithm yields better clustering accuracy than other clustering techniques. (C) 2019 Elsevier Ltd. All rights reserved.

Keyword:

Text mining

Information retrieval

Text clustering

Spectral clustering

Optimization techniques

SK-means

Expectation-Maximization

Particle Swarm Optimization

SCPSO