Clustering Algorithms in Biomedical Research: A Review

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, g...

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Bibliographic Details
Published in:IEEE reviews in biomedical engineering 2010, Vol.3 (1), p.120-154
Main Authors: Rui Xu, Wunsch, Donald C
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Biomedical engineering
Biomedical Research - methods
Cluster Analysis
Clustering
Clustering algorithms
Computational Biology - methods
Data analysis
Data processing
evolutionary computation
Gene expression
Gene Expression Profiling - methods
Image analysis
Magnetic resonance imaging
Mining
neural networks
Terminology
Text analysis
unsupervised learning
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Summary: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.
ISSN:1937-3333
1941-1189
DOI:10.1109/RBME.2010.2083647