Clustering breast cancer data by consensus of different validity indices
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 kn...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
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IET Digital Library
2008
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/28148/ |
| _version_ | 1848793517009141760 |
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| author | Soria, Daniele Garibaldi, Jonathan M. Ambrogi, Federico Lisboa, Paulo J.G. Boracchi, Patrizia Biganzoli, Elia M. |
| author_facet | Soria, Daniele Garibaldi, Jonathan M. Ambrogi, Federico Lisboa, Paulo J.G. Boracchi, Patrizia Biganzoli, Elia M. |
| author_sort | Soria, Daniele |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:01:33Z |
| format | Conference or Workshop Item |
| id | nottingham-28148 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:33Z |
| publishDate | 2008 |
| publisher | IET Digital Library |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-281482020-05-04T20:27:56Z https://eprints.nottingham.ac.uk/28148/ Clustering breast cancer data by consensus of different validity indices Soria, Daniele Garibaldi, Jonathan M. Ambrogi, Federico Lisboa, Paulo J.G. Boracchi, Patrizia Biganzoli, Elia M. 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. IET Digital Library 2008 Conference or Workshop Item PeerReviewed Soria, Daniele, Garibaldi, Jonathan M., Ambrogi, Federico, Lisboa, Paulo J.G., Boracchi, Patrizia and Biganzoli, Elia M. (2008) Clustering breast cancer data by consensus of different validity indices. In: International Conference on Advances in Medical, Signal and Information Processing (4th), 14-16 July 2008, Santa Margherita Ligure, Italy. Clustering algorithms Breast cancer Validity indices http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4609085&filter%3DAND%28p_IS_Number%3A4609057%29%26rowsPerPage%3D75 |
| spellingShingle | Clustering algorithms Breast cancer Validity indices Soria, Daniele Garibaldi, Jonathan M. Ambrogi, Federico Lisboa, Paulo J.G. Boracchi, Patrizia Biganzoli, Elia M. Clustering breast cancer data by consensus of different validity indices |
| title | Clustering breast cancer data by consensus of different validity indices |
| title_full | Clustering breast cancer data by consensus of different validity indices |
| title_fullStr | Clustering breast cancer data by consensus of different validity indices |
| title_full_unstemmed | Clustering breast cancer data by consensus of different validity indices |
| title_short | Clustering breast cancer data by consensus of different validity indices |
| title_sort | clustering breast cancer data by consensus of different validity indices |
| topic | Clustering algorithms Breast cancer Validity indices |
| url | https://eprints.nottingham.ac.uk/28148/ https://eprints.nottingham.ac.uk/28148/ |