Optimized K-Means clustering algorithm using an intelligent stable-plastic variational autoencoder with self-intrinsic cluster validation mechanism

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dc.contributor.authorGikera, Rufus
dc.contributor.authorMambo, Shadrack
dc.contributor.authorMwaura, Jonathan
dc.date.accessioned2025-01-21T07:54:39Z
dc.date.available2025-01-21T07:54:39Z
dc.date.issued2020-09-24
dc.descriptionConference paper in the Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications ICONIC: 2020
dc.description.abstractClustering 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.
dc.identifier.citationGikera, 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.
dc.identifier.other10.1145/3415088.3415125
dc.identifier.urihttps://ir-library.ku.ac.ke/handle/123456789/29442
dc.language.isoen
dc.publisherICONIC
dc.titleOptimized K-Means clustering algorithm using an intelligent stable-plastic variational autoencoder with self-intrinsic cluster validation mechanism
dc.typePresentation
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