MING-HSENG TSENGHUI-CHING WU2026-10-162026-10-162010-01-01Pure ID: 15363474Pure UUID: fb6c1422-3cb8-4da5-a0bb-3527fee0e0d210.1109/ICMLC.2010.5580825https://www.scopus.com/pages/publications/78149294731https://scholars.csmu.edu.tw/handle/123456789/23254The K-means clustering is commonly used in applications of unsupervised classification and the related area due to its simplicity and effectiveness. In this study, an intelligent evolutionary K-means algorithm (IEKA) is firstly developed to optimize the cluster centers by using an improved real-coded genetic algorithm. Then, four cluster validation indices for data clustering are evaluated on six real-life datasets. Finally, experiments are conducted and the performance comparisons of the proposed IEKA approach with other six clustering techniques are reported in this paper.6enjournal article