Efficient layered density-based clustering of categorical data
Journal of Biomedical Informatics
Computational Statistics & Data Analysis
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Science of Computer Programming
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An automatic method to determine the number of clusters using decision-theoretic rough set
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Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Summary: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cutting branches off the dendrogram. A common but inflexible method uses a constant height cutoff value; this method exhibits suboptimal performance on complicated dendrograms. We present the Dynamic Tree Cut R package that implements novel dynamic branch cutting methods for detecting clusters in a dendrogram depending on their shape. Compared to the constant height cutoff method, our techniques offer the following advantages: (1) they are capable of identifying nested clusters; (2) they are flexible—cluster shape parameters can be tuned to suit the application at hand; (3) they are suitable for automation; and (4) they can optionally combine the advantages of hierarchical clustering and partitioning around medoids, giving better detection of outliers. We illustrate the use of these methods by applying them to protein–protein interaction network data and to a simulated gene expression data set. Availability: The Dynamic Tree Cut method is implemented in an R package available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting Contact: stevitihit@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.