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.
Jonathan M. Garibaldi
Paulo J.G. Lisboa
Elia M. Biganzoli
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.
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Soria, D., Garibaldi, J. M., Ambrogi, F., Lisboa, P. J., Boracchi, P., & Biganzoli, E. M. Clustering breast cancer data by consensus of different validity indices|
|Keywords||Clustering algorithms, Breast cancer, Validity indices|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf|
|Additional Information||Published in: 4th IET International Conference on Advances in Medical, Signal and Information Processing, 2008: MEDSIP 2008. IEEE, 2008. ISBN: 978-0-86341-934-8.
© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
You might also like
FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net
Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification
ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems
A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets