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Poonthong, Tanapon
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Electrical & Electronics Engineering

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Non-centroid-based discrete differential evolution for data clustering Poonthong, Tanapon; Wetweerapong, Jeerayut
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8811

Abstract

Data clustering can find similarities and hidden patterns within data. Given a predefined number of groups, most partitional clustering algorithms use representative centers to determine their corresponding clusters. These algorithms, such as K-means and optimization-based algorithms, create and update centroids to give (hyper) spherical shape clusters. This research proposes a non-centroid-based discrete differential evolution (NCDDE) algorithm to solve clustering problems and provide non-spherical shape clusters. The algorithm directs the population of discrete vectors to search for data group labels. It uses a novel discrete mutation strategy analogous to the continuous mutation in classical differential evolution. It also combines a sorting mutation to enhance convergence speed. The algorithm adaptively selects crossover rates in high and low ranges. We use the UCI datasets to compare the NCDDE with other continuous centroid-based algorithms by intra-cluster distance and clustering accuracy. The results show that NCDDE outperforms the compared algorithms overall by intra-cluster distance and achieves the best accuracy for several datasets.
Co-Authors Wetweerapong, Jeerayut