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.
Original language | English |
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Title of host publication | IET Conference Publications |
Edition | 540 CP |
DOIs | |
Publication status | Published - 2008 |
Event | 4th IET International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2008 - Santa Margherita Ligure, Italy Duration: Jul 14 2008 → Jul 16 2008 |
Other
Other | 4th IET International Conference on Advances in Medical, Signal and Information Processing, MEDSIP 2008 |
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Country | Italy |
City | Santa Margherita Ligure |
Period | 7/14/08 → 7/16/08 |
Keywords
- Breast cancer
- Clustering algorithms
- Validity indices
ASJC Scopus subject areas
- Electrical and Electronic Engineering