NDLI: A Novel Approach for Automatic Number of Clusters Detection in Microarray Data Based on Consensus Clustering

Content Provider	IEEE Xplore Digital Library
Author	Nguyen Xuan Vinh Epps, J.
Copyright Year	2009
Abstract	Estimating the true number of clusters in a data set is one of the major challenges in cluster analysis. Yet in certain domains,knowing the true number of clusters is of high importance. For example, in medical research, detecting the true number of groups and sub-groups of cancer would be of utmost importance for their effective treatment. In this paper we propose a novel method to estimate the number of clusters in a micro array data set based on the consensus clustering approach. Although the main objective of consensus clustering is to discover a robust and high quality cluster structure in a data set, closer inspection of the set of clusterings obtained can often give valuable information about the appropriate number of clusters present. More specifically, the set off clusterings obtained when the specified number of clusters coincides with the true number of clusters tends to be less diverse.To quantify this diversity we develop a novel index, namely the Consensus Index (CI), which is built upon a suitable clustering similarity measure such as the well known Adjusted Rand Index (ARI)or our recently developed, information theoretic based index, namely the Adjusted Mutual Information (AMI). Our experiments on both synthetic and real microarray data sets indicate that the CI is a useful indicator for determining the appropriate number of clusters.
Starting Page	84
Ending Page	91
File Size	363837
Page Count	8
File Format	PDF
ISBN	9780769536569
DOI	10.1109/BIBE.2009.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-22
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Clustering methods Robustness Shape Bioinformatics Biomedical engineering Cancer detection Medical treatment Inspection Mutual information adjusted mutual information (AMI) gene clustering model selection number of cluster detection
Content Type	Text
Resource Type	Article

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