Balanced clustering based on collaborative neurodynamic optimization

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.