  This article presents the data used to evaluate the performance of
evolutionary clustering algorithm star (ECA*) compared to five traditional and
modern clustering algorithms. Two experimental methods are employed to examine
the performance of ECA* against genetic algorithm for clustering++
(GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation
(EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32
heterogenous and multi-featured datasets to determine which one performs well
on the three tests. For one, ther paper examines the efficiency of ECA* in
contradiction of its corresponding algorithms using clustering evaluation
measures. These validation criteria are objective function and cluster quality
measures. For another, it suggests a performance rating framework to measurethe
the performance sensitivity of these algorithms on varos dataset features
(cluster dimensionality, number of clusters, cluster overlap, cluster shape and
cluster structure). The contributions of these experiments are two-folds: (i)
ECA* exceeds its counterpart aloriths in ability to find out the right cluster
number; (ii) ECA* is less sensitive towards dataset features compared to its
competitive techniques. Nonetheless, the results of the experiments performed
demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based
on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA*
on several real applications has not been achieved yet.
