An elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping similar documents

• Published in: Journal of ambient intelligence and humanized computing Vol. 13; no. 4; pp. 1925 - 1939
• Main Authors: Thirumoorthy, K., Muneeswaran, K.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Clustering; Computational Intelligence; Data mining; Datasets; Documents; Elitism; Engineering; Feature selection; Genetic algorithms; Internet; Methods; Optimization algorithms; Optimization techniques; Original Research; Particle swarm optimization; Robotics and Automation; Similarity measures; User Interfaces and Human Computer Interaction; Self-adaptive; Poor and Rich optimization; Multi-population; Grouping similar documents; Elitism
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-021-02955-x

In this digital era, grouping similar documents from the archives on the web is a difficult and computationally expensive task. In this paper, we propose an elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping the similar documents, referred to as ESAMPRO. The objective function of the proposed work maximizes the accuracy and minimize the intra cluster distance. The proposed algorithm is evaluated using the various extrinsic cluster quality metrics. An in-depth analysis of the experimental results on four supervised benchmark datasets confirms that the proposed ESAMPRO algorithm outperformed the five well-known document clustering algorithms such as K-means, particle swarm optimization, whale optimization, dragonfly and grey wolf optimization algorithm.