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.
Clustering breast cancer data by consensus of different validity indices / D. Soria, J. Garibaldi, F. Ambrogi, P. Lisboa, P. Boracchi, E. Biganzoli - In: 4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008)[s.l] : IET Pubblicazioni Conferenza, 2008. - ISBN 978 0 86341 934 8. - pp. 125-125 [10.1049/cp:20080437]
Clustering breast cancer data by consensus of different validity indices
F. Ambrogi;P. BoracchiPenultimo
;E. BiganzoliUltimo
2008
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.Pubblicazioni consigliate
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