Clustering breast cancer data by consensus of different validity indices
Soria, Daniele and Garibaldi, Jonathan M. and Ambrogi, Federico and Lisboa, Paulo J.G. and 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.
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4609085&filter%3DAND%28p_IS_Number%3A4609057%29%26rowsPerPage%3D75
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.
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