  Clustering is a commonly used method for exploring and analysing data where
the primary objective is to categorise observations into similar clusters. In
recent decades, several algorithms and methods have been developed for
analysing clustered data. We notice that most of these techniques
deterministically define a cluster based on the value of the attributes,
distance, and density of homogenous and single-featured datasets. However,
these definitions are not successful in adding clear semantic meaning to the
clusters produced. Evolutionary operators and statistical and
multi-disciplinary techniques may help in generating meaningful clusters. Based
on this premise, we propose a new evolutionary clustering algorithm (ECAStar)
based on social class ranking and meta-heuristic algorithms for stochastically
analysing heterogeneous and multiple-featured datasets. The ECAStar is
integrated with recombinational evolutionary operators, Levy flight
optimisation, and some statistical techniques, such as quartiles and
percentiles, as well as the Euclidean distance of the K-means algorithm.
Experiments are conducted to evaluate the ECAStar against five conventional
approaches: K-means (KM), K-meansPlusPlus (KMPlusPlus), expectation
maximisation (EM), learning vector quantisation (LVQ), and the genetic
algorithm for clusteringPlusPlus (GENCLUSTPlusPlus).
