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 |
|---|---|
| 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 |
|---|---|
| Country | Italy |
| City | Santa Margherita Ligure |
| Period | 7/14/08 → 7/16/08 |
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Keywords
- Breast cancer
- Clustering algorithms
- Validity indices
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Clustering breast cancer data by consensus of different validity indices. / Soria, D.; Garibaldi, J. M.; Ambrogi, F.; Lisboa, P. J G; Boracchi, P.; Biganzoli, E.
IET Conference Publications. 540 CP. ed. 2008.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Clustering breast cancer data by consensus of different validity indices
AU - Soria, D.
AU - Garibaldi, J. M.
AU - Ambrogi, F.
AU - Lisboa, P. J G
AU - Boracchi, P.
AU - Biganzoli, E.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Breast cancer
KW - Clustering algorithms
KW - Validity indices
UR - http://www.scopus.com/inward/record.url?scp=67549102287&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67549102287&partnerID=8YFLogxK
U2 - 10.1049/cp:20080437
DO - 10.1049/cp:20080437
M3 - Conference contribution
AN - SCOPUS:67549102287
SN - 9780863419348
BT - IET Conference Publications
ER -