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Journal of Healthcare Engineering
Volume 2017, Article ID 5284145, 7 pages
Research Article

2-Way k-Means as a Model for Microbiome Samples

1Department of Computer Science, Columbia University, New York, NY 10027, USA
2Department of Biological Sciences, Columbia University, New York, NY 10027, USA

Correspondence should be addressed to Weston J. Jackson; moc.liamg@noskcaj.j.notsew

Received 20 May 2017; Accepted 17 July 2017; Published 5 September 2017

Academic Editor: Ahmad P. Tafti

Copyright © 2017 Weston J. Jackson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.