An improved differential evolution with cluster decomposition algorithm for automatic clustering

This study proposes an improved differential evolution (DE) algorithm to solve an automatic clustering problem. Automatic clustering is a clustering technique in data mining which can automatically define the best number of clusters, as well as construct those clusters. In order to overcome the weakness of the DE algorithm for automatic clustering, some improvement scenarios are proposed. They include the best solution effect, saturated solution, acceleration and handling downhill scenarios. The best solution effect and acceleration factor are proposed to boost the convergence of the DE algorithm. The saturated solution helps the algorithm maintain the diversity of the population. The handling downhill scenario allows chromosomes to move to a worse solution, since it might later lead to a better solution. The proposed algorithm is evaluated using four well-known datasets. The computational results indicate that the proposed improvements can enhance the DE algorithm's performance. In addition, the proposed method is also applied to cluster customers for a ladies' office wear clothing franchise. The results can be used to plan a marketing strategy for the case company.