Scope of Research on Particle Swarm Optimization Based Data Clustering
This work addresses the problem of improving clustering accuracy for multidimensional data, which is incremental as it builds on existing PSO methods.
The paper identifies that Particle Swarm Optimization (PSO) variants perform poorly on multidimensional data clustering and proposes a hybrid PSO strategy to address this challenge for numerical, text, and image data.
Optimization is nothing but a mathematical technique which finds maxima or minima of any function of concern in some realistic region. Different optimization techniques are proposed which are competing for the best solution. Particle Swarm Optimization (PSO) is a new, advanced, and most powerful optimization methodology that performs empirically well on several optimization problems. It is the extensively used Swarm Intelligence (SI) inspired optimization algorithm used for finding the global optimal solution in a multifaceted search region. Data clustering is one of the challenging real world applications that invite the eminent research works in variety of fields. Applicability of different PSO variants to data clustering is studied in the literature, and the analyzed research work shows that, PSO variants give poor results for multidimensional data. This paper describes the different challenges associated with multidimensional data clustering and scope of research on optimizing the clustering problems using PSO. We also propose a strategy to use hybrid PSO variant for clustering multidimensional numerical, text and image data.