title: Clustering algorithms in biomedical research: a review.
abstract: Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.
authors.affiliation: Industrial Artificial Intelligence Laboratory, GE Global Research Center, Niskayuna, NY 12309, USA. rxu@ieee.org
authors.forename: Rui
authors.initials: R
authors.lastname: Xu
authors.forename: Donald C
authors.initials: DC
authors.lastname: Wunsch
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000379:methods*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods