Performance Evaluation Results of evolutionary clustering algorithm star for clustering heterogeneous datasets
The data was collected from the written Java codes by the authors, and Weka packages for executing ECA* on 32 heterogenous and multi-featured datasets against its counterpart algorithms (KM, KM++, EM, LVQ, and GENCLUST++). Each of these algorithms was run thirty times on each of the 32 benchmarking dataset problems to evaluate the performance of each of them in three tests. First, evaluating the performance of ECA* against its counterpart algorithms using internal and external measuring criteria. Second, analysing the statistical performance of ECA* compare to its counterpart algorithms based on their best solution (best), the worst solution (worst), and the mean solution (average) for intraCluster distance, interCluster distance, and execution time. Final, proposing a performance framework to investigate how sensitive the performance of these algorithms on different dataset features, such as cluster overlap, number of clusters, cluster dimensionality, and cluster structure, and cluster shape.