Ant-based sorting and ACO-based clustering approaches: A review

Abstract

Data clustering is used in a number of fields including statistics, bioinformatics, machine learning exploratory data analysis, image segmentation, security, medical image analysis, web handling and mathematical programming. Its role is to group data into clusters with high similarity within clusters and with high dissimilarity between clusters. This paper reviews the problems that affect clustering performance for deterministic clustering and stochastic clustering approaches. In deterministic clustering, the problems are caused by sensitivity to the number of provided clusters. In stochastic clustering, problems are caused either by the absence of an optimal number of clusters or by the projection of data. The review is focused on ant-based sorting and ACO-based clustering which have problems of slow convergence, un-robust results and local optima solution. The results from this review can be used as a guide for researchers working in the area of data clustering as it shows the strengths and weaknesses of using both clustering approaches.