Literature DB >> 11674852

Resampling method for unsupervised estimation of cluster validity.

E Levine1, E Domany.   

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.

Mesh:

Year:  2001        PMID: 11674852     DOI: 10.1162/089976601753196030

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  22 in total

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3.  Clustering approach to identify intratumour heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma.

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4.  Identifying prototypical components in behaviour using clustering algorithms.

Authors:  Elke Braun; Bart Geurten; Martin Egelhaaf
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5.  A highly efficient multi-core algorithm for clustering extremely large datasets.

Authors:  Johann M Kraus; Hans A Kestler
Journal:  BMC Bioinformatics       Date:  2010-04-06       Impact factor: 3.169

6.  Merged consensus clustering to assess and improve class discovery with microarray data.

Authors:  T Ian Simpson; J Douglas Armstrong; Andrew P Jarman
Journal:  BMC Bioinformatics       Date:  2010-12-03       Impact factor: 3.169

7.  Expression profiles of acute lymphoblastic and myeloblastic leukemias with ALL-1 rearrangements.

Authors:  T Rozovskaia; O Ravid-Amir; S Tillib; G Getz; E Feinstein; H Agrawal; A Nagler; E F Rappaport; I Issaeva; Y Matsuo; U R Kees; T Lapidot; F Lo Coco; R Foa; A Mazo; T Nakamura; C M Croce; G Cimino; E Domany; E Canaani
Journal:  Proc Natl Acad Sci U S A       Date:  2003-06-02       Impact factor: 11.205

8.  Speeding up the Consensus Clustering methodology for microarray data analysis.

Authors:  Raffaele Giancarlo; Filippo Utro
Journal:  Algorithms Mol Biol       Date:  2011-01-14       Impact factor: 1.405

9.  A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data.

Authors:  Christopher E Hart; Lucas Sharenbroich; Benjamin J Bornstein; Diane Trout; Brandon King; Eric Mjolsness; Barbara J Wold
Journal:  Nucleic Acids Res       Date:  2005-05-10       Impact factor: 16.971

10.  Model order selection for bio-molecular data clustering.

Authors:  Alberto Bertoni; Giorgio Valentini
Journal:  BMC Bioinformatics       Date:  2007-05-03       Impact factor: 3.169

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