Custering Results of evolutionary clustering algorithm star for clustering heterogeneous datasets

Published: 09-03-2021| Version 2 | DOI: 10.17632/bsn4vh3zv7.2
Tarik A. Rashid,
Dr Bryar Hassan


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 ECA* against its competitve algorithms.