Text document clustering using Spectral Clustering algorithm with Particle Swarm Optimization. (15th November 2019)

• Record Type: Journal Article
• Authors: Janani, R.; Vijayarani, S.
• Is Part Of: Expert systems with applications. Volume 134(2019)
• Page Start: 192
• Page End: 200
• Publication Date: 2019-11-15
• Subjects: Text mining -- Information retrieval -- Text clustering -- Spectral clustering -- Optimization techniques -- SK-means -- Expectation-Maximization -- Particle Swarm Optimization -- SCPSO; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.05.030
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An automatic text clustering framework for handling unstructured documents. Spectral Clustering algorithm SCPSO is proposed based on Particle Swarm Optimization. The proposed method is able to group the documents based on their content. Cluster Purity is improved by using SCPSO algorithm, while the number of clusters increased. The proposed method SCPSO outperforms three challenging methods in terms of Cluster Purity. Abstract: 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.