期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2013
卷号：5
期号：5
页码：57
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides anomenclature that highlights some aspects that are very important in the context of evolutionary dataclustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi ObjectiveEvolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges ofMOEA design and data clustering, along with conclusions and recommendations for novice andresearchers by positioning most promising paths of future research.MOEAs have substantial success across a variety of MOP applications, from pedagogical multifunctionoptimization to real-world engineering design. The survey paper noticeably organizes the developmentswitnessed in the past three decades for EAs based metaheuristics to solve multiobjective optimizationproblems (MOP) and to derive significant progression in ruling high quality elucidations in a single run.Data clustering is an exigent task, whose intricacy is caused by a lack of unique and precise definition of acluster. The discrete optimization problem uses the cluster space to derive a solution for Multiobjectivedata clustering. Discovery of a majority or all of the clusters (of illogical shapes) present in the data is along-standing goal of unsupervised predictive learning problems or exploratory pattern analysis.
