Gikera, RufusMambo, ShadrackMwaura, Jonathan2025-01-212025-01-212020-09-24Gikera, Rufus & Kenya, Nairobi & Mambo, Shadrack & Mwaura, Jonathan. (2020). Optimized K-Means clustering algorithm using an intelligent stable-plastic variational autoencoder with self-intrinsic cluster validation mechanism. 1-11. 10.1145/3415088.3415125.10.1145/3415088.3415125https://ir-library.ku.ac.ke/handle/123456789/29442Conference paper in the Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications ICONIC: 2020Clustering is one of the most important tasks in exploratory data analysis [1, 55, 59]. K-means are the most popular clustering algorithms [51, 61]. This is because of their ability to adapt to new examples and to scale up to large datasets. They are also easily understandable and computationally faster [57, 60, 3, 62]. However, the number of clusters, K, has to be specified by the user [50]. Random process is the norm of searching for appropriate number of clusters, until convergence [53, 5]. Several variants of the k-means algorithm have been proposed, geared towards optimal selection of the K [8, 48]. The objective of this paper is to analyze the scaling up problems associated with these variants for optimizing K in the k-means clustering algorithms. Finally, a more enhanced hybrid autoencoder-based k-means will be developed and evaluated against the existing variants.enPresentation