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The present survey provides the state-of-the-art of research, copiouslydevoted to Evolutionary Approach (EAs) for clustering exemplified with adiversity of evolutionary computations. The Survey provides a nomenclature thathighlights some aspects that are very important in the context of evolutionarydata clustering. The paper missions the clustering trade-offs branched out withwide-ranging Multi Objective Evolutionary Approaches (MOEAs) methods. Finally,this study addresses the potential challenges of MOEA design and dataclustering, along with conclusions and recommendations for novice andresearchers by positioning most promising paths of future research. MOEAs havesubstantial success across a variety of MOP applications, from pedagogicalmultifunction optimization to real-world engineering design. The survey papernoticeably organizes the developments witnessed in the past three decades forEAs based metaheuristics to solve multiobjective optimization problems (MOP)and to derive significant progression in ruling high quality elucidations in asingle run. Data clustering is an exigent task, whose intricacy is caused by alack of unique and precise definition of a cluster. The discrete optimizationproblem uses the cluster space to derive a solution for Multiobjective dataclustering. Discovery of a majority or all of the clusters (of illogicalshapes) present in the data is a long-standing goal of unsupervised predictivelearning problems or exploratory pattern analysis.

international journal of computer science and information technology/international journal of computer science and information technology  (chennai. print)

A preliminary survey on optimized multiobjective metaheuristic methods for data clustering using evolutionary approaches