A modified differential evolution-based fuzzy multi-objective approach for clustering

Many evolutionary‐based metaheuristics have been proposed for minimisation of intra cluster distance for better clustering, however the effect of inter cluster distances on clustering is ignored in most of the cases. Considering this issue a modified differential evolution‐based fuzzy multi‐objective (MDEFM) approach is proposed in this study where effect of both intra and inter cluster distance on clustering is analysed. This way of considering both the objectives and assigning different weighting factors according to their priority results in well separate clusters with greater accuracy. Furthermore, a centroid rearrangement scheme has been proposed for getting a consistent result. A comparative analysis of the proposed approach with another five population‐based methods on eight real datasets is carried out to justify the efficacy of the model. The results reveal that the proposed approach can be considered as one of the alternate powerful methods for data clustering applications in various fields.