Search

Clustering is an essential data exploration technique applied to many disciplines and applications such as data mining, image processing, bioinformatics, and machine learning. A clustering method identifies hidden patterns in a dataset and combines similar data points into clusters. The problems are challenging when they have many data points, attributes, and cluster partitions. In this paper, we propose an adaptive differential evolution with an archive strategy (ADEAS) to find candidate centroids and minimize their intra-cluster distance for solving partitional clustering problems. The archiving strategy stores inferior solutions during the selection operation to increase population diversity and create directions for guiding the search. We validate the proposed algorithm with several well-known methods using the UCI datasets. The results show that ADEAS outperforms the compared methods. [ABSTRACT FROM AUTHOR]