Dear Murray,
It depends on the type of data you are interested to cluster. If you are working with the longitudinal survival data then there are many prognostic techniques to achieve this goal. For instance we published extensively on 'clustering classified survival data'. Methods includes Phase type survival tree based clustering, Mixed distribution survival trees based clustering, Extended phase type distribution survival trees based clustering. Some of these methods (especially mixed distribution) are also applicable on other types of longitudinal data. Some of our papers you might find interesting are:
1. Garg L, McClean SI, Meenan BJ, Millard PH (2011). Phase-type survival trees and mixed distribution survival trees for clustering patients’ hospital length of stay. INFORMATICA. 22(1): 57-72.
2. Garg L., McClean SI, Barton M, Meenan BJ and Fullerton K (2010). The extended mixture distribution survival tree based analysis for clustering and patient pathway prognostication in a stroke care unit. International Journal of Information Sciences and Application. 2(4): 671-675.
3. Garg L, McClean SI, Meenan BJ, Millard PH (2009). A phase-type survival tree model for clustering patients according to their hospital length of stay. Proceedings of the XIII International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2009), pp. 497-502, Eds: Leonidas Sakalauskas, Christos Skiadas, Edmundas K. Zavadskas, ISBN: 978-9955-28-463-5, Publisher: Vilnius Gediminas Technical University Press.
4. Garg L, McClean SI, Barton M, Meenan BJ, Fullerton K (2010). Patient pathway prognostication using the extended mixed distribution survival tree based analysis, the Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2010), Chania Crete Greece, June 8-11, 2010.
5. Garg L, McClean SI, Barton M, Meenan BJ, Fullerton K (2010). An extended phase type survival tree for patient pathway prognostication. The IEEE Workshop on Health Care Management (WHCM2010), Venice, Italy, February 18-20. DOI: 10.1109/WHCM.2010.5441242.
I hope that you would find these useful. Please feel free to ask if you require further help.
Best regards,
Lalit Garg
Machine Learning Research Lab
Division of Control and Instrumentation,
School of Electrical and Electronic Engineering (SEEE),
College of Engineering,
Nanyang Technological University, Singapore
Web: http://lalitgarg.info/
Mobile: +65-86142709
________________________________________
From: A UK-based worldwide e-mail broadcast system mailing list [[log in to unmask]] on behalf of Murray Jorgensen [[log in to unmask]]
Sent: Sunday, September 18, 2011 6:42 AM
To: [log in to unmask]
Subject: Clustering classified data
I would like to ask the list a question which seems to subtle to yield
to Google. Suppose we wish to cluster multivariate data that are already
classified. Certainly data in the same group of the classification
should be in the same cluster but we may like to find a coarser
clustering in which some of the classification groups are merged.
I can think of a few things that might be done but I wonder if anyone
can point me to papers on such questions - theoretical or applied?
Cheers, Murray
--
Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
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