Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model

作     者：Dodda, Ratnam Babu, A. Suresh 

作者机构：Jawaharlal Nehru Technol Univ Dept Comp Sci & Engn Anantapur India 

出 版 物：《INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS》 (国际人工智能工具杂志)

年 卷 期：2024年第33卷第1期

页      面：2350061-2350061页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Dirichlet process k-means clustering machine learning particle swarm optimizer text document clustering 

摘      要：In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.