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Determining the number of clusters using penalised k-means clustering
Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613501320593408 |
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| access_status_str | Open Access |
| author2 | Millard, Sollie M. |
| author_browse | Millard, Sollie M. |
| author_facet | Millard, Sollie M. |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/100627 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:09.154Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/100627 Determining the number of clusters using penalised k-means clustering Millard, Sollie M. robert.w.greyling@gmail.com Kanfer, F.H.J. (Frans) Greyling, Robert William UCTD K-means Unsupervised k-means Entropy Pre-intialisation Number of clusters Dissertation (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. Statistics MSc (Advanced Data Analytics) Unrestricted Faculty of Natural and Agricultural Sciences None 2025-02-10T07:15:08Z 2025-02-10T07:15:08Z 2025-04 2024-11 Dissertation * A2025 http://hdl.handle.net/2263/100627 https://doi.org/10.25403/UPresearchdata.28380005 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. application/pdf University of Pretoria |
| spellingShingle | UCTD K-means Unsupervised k-means Entropy Pre-intialisation Number of clusters Determining the number of clusters using penalised k-means clustering |
| title | Determining the number of clusters using penalised k-means clustering |
| title_full | Determining the number of clusters using penalised k-means clustering |
| title_fullStr | Determining the number of clusters using penalised k-means clustering |
| title_full_unstemmed | Determining the number of clusters using penalised k-means clustering |
| title_short | Determining the number of clusters using penalised k-means clustering |
| title_sort | determining the number of clusters using penalised k means clustering |
| topic | UCTD K-means Unsupervised k-means Entropy Pre-intialisation Number of clusters |
| url | http://hdl.handle.net/2263/100627 https://doi.org/10.25403/UPresearchdata.28380005 |