Ashraf, Faisal BinMatin A.Shafi M.S.R.Islam M.U.2026-09-172026-09-172021-01-01F. B. Ashraf, A. Matin, M. S. R. Shafi and M. U. Islam, "An Improved K-means Clustering Algorithm for Multi-dimensional Multi-cluster data Using Meta-heuristics," 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2021, pp. 1-6, doi: 10.1109/ICCIT54785.2021.9689836.97816654943592-s2.0-85125006458https://hdl.handle.net/10361/30028k-means is the most widely used clustering algorithm which is an unsupervised technique that needs assumptions of centroids to begin the process. Hence, the problem is NP-hard and needs careful consideration and optimization to get a better quality of clusters of data. In this work, a meta-heuristic based genetic algorithm is proposed to optimize the centroid initialization process. The proposed method includes tournament selection, probability-based mutation, and elitism that leads to finding the optimal centroids for the clusters of a given dataset. Nine different and diversified datasets were used to test the performance of the proposed method in terms of the davies-bouldin index and it performed better in all the datasets than the standard k-means and minibatch k-means algorithm.6 Pagesen-USMetaheuristicsClustering algorithmsInformation technologyGenetic algorithmsK-meansClustering algorithmMeta-heuristicGenetic algorithmCluster analysis.Machine learning.Conference Proceeding10.1109/ICCIT54785.2021.9689836