  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.
