A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Problems in gene clustering based on gene expression data
Journal of Multivariate Analysis
Journal of Multivariate Analysis
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Quality indices for (practical) clustering evaluation
Intelligent Data Analysis
A metric to evaluate a cluster by eliminating effect of complement cluster
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A new asymmetric criterion for cluster validation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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Data resampling techniques are increasingly used for assigning confidence to clustering results, in particular for tumor class discovery based on genomic data. One factor that determines the success of this approach is the capability of a resampling scheme to simulate the sampling variability by using the information of sparse sample data. We present a method for evaluating resampling performance based on model simulations. This method was applied to results of 40 cluster validity indices and one partition stability index obtained from 12 clustering procedures including different distance measures. The results were generated for benchmark data of five statistical models, gene expression profiles of three multi-class tumor sample data sets, four data sets of the widely used UCI repository, and spatiotemporal neuroimaging data. The results suggest a ranking of the three resampling techniques analyzed: perturbation (adding noise to the data) was more effective than subsampling and both clearly outperformed the bootstrapping technique in the detection of correct clustering consensus results. Due to the consistency of the results this ranking may have impact on the selection of a resampling method for the cluster validation in future studies. Moreover, intelligent control of the resampling parameters can increase the achievable confidence in clustering results.