Millard, Sollie M.2025-02-102025-02-102025-042024-11*A2025http://hdl.handle.net/2263/100627Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2024.Clustering is an important part of statistics. However the issue of pre-initialisation of the number of clusters is still persistent. In this minor dissertation we consider a procedure to eliminate the pre-initialisation of the number of clusters in the k-means algorithm. This important advancement reduces manual effort in clustering tasks. This procedure aims to automatically eliminate the determination of the correct value of k. Following the approach by Sinaga and Yang; we modify the traditional k-means objective function by adding two entropy terms as penalty terms. An additional step was added to the algorithm to ensure that the initial clusters are not empty. A simulation study was conducted using multiple datasets with varying true cluster counts k, data dimensionalities D, and sample sizes n. Results indicate that the proposed algorithm performs well in identifying distinct clusters, particularly in lower-dimensional data.en© 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.UCTDK-meansUnsupervised k-meansEntropyPre-intialisationNumber of clustersDissertationu17104892https://doi.org/10.25403/UPresearchdata.28380005