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A New Particle Swarm Optimization Algorithm for Optimizing Big Data Clustering

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Abstract

Clustering is an ideal tool for working with big data and searching for structures in the data set. Clustering aims at maximizing the similarity between the data within a cluster and minimizing the similarity between the data between different clusters. This study presents a new and improved Particle Swarm Optimization (PSO) algorithm using pattern reduction and reducing the clustering calculation time with Multistart Pattern Reduction-Enhanced PSO (MPREPSO). This method adds two pattern reduction operators and multistart operators into the PSO algorithms. The goal of the pattern reduction operator is to reduce the computational time from the compression of static patterns. The purpose of the multistart operator is to avoid falling into the local optimal by enforcing diversity in the population. Two pattern reduction and multistart operators are combined with the PSO algorithm to evaluate the performance of this method.

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Authors and Affiliations

  1. Department of Industrial Engineering, Islamic Azad University Arak Branch, Arak, Iran

    Seyed Emadedin Hashemi & Maryam Bakhshi

  2. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, USA

    Madjid Tavana

  3. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, Paderborn, Germany

    Madjid Tavana

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This article is part of the topical collection “Advanced Computing and Data Sciences” guest edited by Mayank Singh, Vipin Tyagi and P.K. Gupta.

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Hashemi, S.E., Tavana, M. & Bakhshi, M. A New Particle Swarm Optimization Algorithm for Optimizing Big Data Clustering. SN COMPUT. SCI. 3, 311 (2022). https://doi.org/10.1007/s42979-022-01208-8

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