Clustering 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 ECA* against its competitve algorithms.

本数据集源自作者编写的Java代码，以及用于在32个异构多特征数据集上运行ECA*算法，并与对比算法（KM、KM++、期望最大化算法（EM）、学习向量量化（LVQ）及GENCLUST++）开展性能对比的Weka工具包。为评估ECA*算法相较于上述对比算法的性能表现，所有算法均在32个基准数据集任务上各运行30次。