title: Bagging to improve the accuracy of a clustering procedure.
abstract: BACKGROUND
  The microarray technology is increasingly being applied in biological and medical research to address a wide range of problems such as the classification of tumors. An important statistical question associated with tumor classification is the identification of new tumor classes using gene expression profiles. Essential aspects of this clustering problem include identifying accurate partitions of the tumor samples into clusters and assessing the confidence of cluster assignments for individual samples.
  
  
  RESULTS
  Two new resampling methods, inspired from bagging in prediction, are proposed to improve and assess the accuracy of a given clustering procedure. In these ensemble methods, a partitioning clustering procedure is applied to bootstrap learning sets and the resulting multiple partitions are combined by voting or the creation of a new dissimilarity matrix. As in prediction, the motivation behind bagging is to reduce variability in the partitioning results via averaging. The performances of the new and existing methods were compared using simulated data and gene expression data from two recently published cancer microarray studies. The bagged clustering procedures were in general at least as accurate and often substantially more accurate than a single application of the partitioning clustering procedure. A valuable by-product of bagged clustering are the cluster votes which can be used to assess the confidence of cluster assignments for individual observations.
  
  
  BACKGROUND
  For supplementary information on datasets, analyses, and software, consult http://www.stat.berkeley.edu/~sandrine and http://www.bioconductor.org.
authors.affiliation: Division of Biostatistics, School of Public Health, University of California, Berkeley, 140 Earl Warren Hall, 7360, Berkeley, CA 94720-7360, USA. sandrine@stat.berkeley.edu
authors.forename: Sandrine
authors.initials: S
authors.lastname: Dudoit
authors.forename: Jane
authors.initials: J
authors.lastname: Fridlyand
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D006801:Humans; D007938:Leukemia / Q000235:genetics*; D008545:Melanoma / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity