Balanced clustering based on collaborative neurodynamic optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Article number109026
Journal / PublicationKnowledge-Based Systems
Volume250
Online published21 May 2022
Publication statusPublished - 17 Aug 2022

Abstract

Balanced clustering is a semi-supervised learning approach to data preprocessing. This paper presents a collaborative neurodynamic algorithm for balanced clustering. The balanced clustering problem is formulated as a combinatorial optimization problem and reformulated as an Ising model. A collaborative neurodynamic algorithm is developed to solve the formulated balanced clustering problem based on a population of discrete Hopfield networks or Boltzmann machines reinitialized upon their local convergence by using a particle swarm optimization rule. The algorithm inherits the desirable property of almost-sure convergence of collaborative neurodynamic optimization. Experimental results on six benchmark datasets are elaborated to demonstrate the superior convergence and performance of the proposed algorithm against four existing balanced clustering algorithms in terms of balanced clustering quality.

Research Area(s)

  • Balanced clustering, Boltzmann machines, Collaborative neurodynamic optimization, Combinatorial optimization, Hopfield networks