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Algorithms for clustering data
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Class prediction and discovery using gene expression data
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Artificial Intelligence in Medicine
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IEEE Transactions on Information Technology in Biomedicine
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ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments
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Gene selection using rough set theory
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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Average parameterization and partial kernelization for computing medians
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Generalized Adjusted Rand Indices for cluster ensembles
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MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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Cluster ensembles via weighted graph regularized nonnegative matrix factorization
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In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data. The method can best be thought of as an analysis approach, to guide and assist in the use of any of a wide range of available clustering algorithms. We call the new methodology consensus clustering, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters. The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a visualization tool to inspect cluster number, membership, and boundaries. We present the results of our experiments on both simulated data and real gene expression data aimed at evaluating the effectiveness of the methodology in discovering biologically meaningful clusters.