Soria, Daniele, Garibaldi, J.M., Ambrogi, F., Lisboa, P.J.G., Boracchi, P., Biganzoli, E. (2008) Clustering breast cancer data by consensus of different validity indices. In: 4th IET International Conference on Advances in Medical, Signal and Information Processing - MEDSIP 2008. (540 CP). (doi:10.1049/cp:20080437) (KAR id:98906)
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| Official URL: https://doi.org/10.1049/cp:20080437 |
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Abstract
Clustering algorithms will, in general, either partition a given data set into a pre-specified number of clusters or will produce a hierarchy of clusters. In this paper we analyse several different clustering techniques and apply them to a particular data set of breast cancer data. When we do not know a priori which is the best number of groups, we use a range of different validity indices to test the quality of clustering results and to determine the best number of clusters. While for the K-means method there is not absolute agreement among the indices as to which is the best number of clusters, for the PAM algorithm all the indices indicate 4 as the best cluster number.
| Item Type: | Conference or workshop item (Paper) |
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| DOI/Identification number: | 10.1049/cp:20080437 |
| Additional information: | cited By 3 |
| Uncontrolled keywords: | Clustering algorithms, Breast cancer, Va�lidity indices |
| Subjects: | Q Science > QA Mathematics (inc Computing science) |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Daniel Soria |
| Date Deposited: | 08 Dec 2022 15:27 UTC |
| Last Modified: | 05 Nov 2024 13:04 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/98906 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Soria, Daniele.
| Creator's ORCID: |
 https://orcid.org/0000-0002-0164-8218
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