Resampling Method for Unsupervised Estimation of Cluster Validity
Open Access
- 1 November 2001
- journal article
- research article
- Published by MIT Press in Neural Computation
- Vol. 13 (11) , 2573-2593
- https://doi.org/10.1162/089976601753196030
Abstract
We introduce a method for validation of results obtained by clustering analysis of data. The method is based on resampling the available data. A figure of merit that measures the stability of clustering solutions against resampling is introduced. Clusters that are stable against resampling give rise to local maxima of this figure of merit. This is presented first for a one-dimensional data set, for which an analytic approximation for the figure of merit is derived and compared with numerical measurements. Next, the applicability of the method is demonstrated for higher-dimensional data, including gene microarray expression data.Keywords
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This publication has 14 references indexed in Scilit:
- Coupled two-way clustering analysis of gene microarray dataProceedings of the National Academy of Sciences, 2000
- Super-paramagnetic clustering of yeast gene expression profilesPhysica A: Statistical Mechanics and its Applications, 2000
- Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arraysProceedings of the National Academy of Sciences, 1999
- Superparamagnetic clustering of data — The definitive solution of an ill-posed problemPhysica A: Statistical Mechanics and its Applications, 1999
- Exploring the new world of the genome with DNA microarraysNature Genetics, 1999
- Accessing Genetic Information with High-Density DNA ArraysScience, 1996
- Superparamagnetic Clustering of DataPhysical Review Letters, 1996
- On some significance tests in cluster analysisJournal of Classification, 1985
- A Cluster Separation MeasureIEEE Transactions on Pattern Analysis and Machine Intelligence, 1979
- A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated ClustersJournal of Cybernetics, 1973