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Sharma, A., Podolsky, R., Zhao, J. and McIndoe, R.A. (2009) A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets. Bioinformatics, 25, 1152-1157. doi:10.1093/bioinformatics/btp123

has been cited by the following article:

• TITLE: Estimating the number of data clusters via the contrast statistic

  AUTHORS: Yuriy Lyakh, Vitaliy Gurianov, Oleg Gorshkov, Yuriy Vihovanets

  KEYWORDS: SOM Neural Network; Clustering; Gap Statistic; Silhouette Statistic

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.5 No.2, February 28, 2012

  ABSTRACT: A new method (the Contrast statistic) for estimating the number of clusters in a set of data is proposed. The technique uses the output of self-organising map clustering algorithm, comparing the change in dependency of “Contrast” value upon clusters number to that expected under a uniform distribution. A simulation study shows that the Contrast statistic can be used successfully either, when variables describing the object in a multi-dimensional space are independent (ideal objects) or dependent (real biological objects).