Cluster Analysis Using Improved Bio-inspired Fuzzy C-Means Algorithm

Abstract

Optimization is a vital constituent of machine learning as it brings about efficiency of the analysis process. An optimizer is used to minimize an objective function, which in turn leverages efficiency of any given model. Many metaheuristic algorithms have been applied on many models for optimization purposes. The mostly favoured metaheuristic algorithms are the population-based ones as they initialize a population of candidate solutions, rather than a single-solution strategy. This paper takes off from the original research made on fuzzy C-means (FCM), where the weighting component (m) was set at 2. Setting the weighting component at 2 may not ideally work well for all applications. Then, the point of this examination is to optimize the weighting component m using bio-inspired algorithms that are population-based in nature. The idea is to compare performance of conventional FCM with improved version of FCM by augmenting it with different optimization techniques. Three comparatively new and popular bio-inspired optimization algorithms such as moth-flame Optimizer (MFO), gray wolf optimizer (GWO) and whale optimization algorithm (WOA) are selected to optimize tunable parameters of conventional FCM. For experimentation purpose, five different data sets namely seeds, iris, glass identification, mall customer segmentation and BuddyMove are selected. The experimental results show considerable improvements in the overall performance of conventional FCM as a consequence of parameter optimization. A new approach for optimizing FCM parameters using bio-inspired algorithms is presented.