A Review of Evolutionary Multi-objective Clustering Approaches

Abstract

Evolutionary multi-objective clustering (EMOC), a modern clustering technique, has been widely applied to extract patterns, allowing us to analyze different aspects of complex data by considering multiple criteria. In this article, we present an analysis of the advances in EMOC studies and provide a profile of this study field by considering an extensive mapping of the literature to identify the main methods and concepts that have been adopted to design the EMOC approaches. This review provides a comprehensive view of the EMOC studies that supports newcomers or busy researchers in understanding the general features of the existing algorithms and guides the generation of new approaches. For that, we introduce a general architecture of EMOC to describe the main elements applied in designing EMOC algorithms and we correlate them with the main features found in the literature. Also, we categorized the EMOC algorithms based on shared characteristics that highlight the main features or application fields. The paper ends by addressing some potential subjects for future research.

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