Bayesian Biclustering on Discrete Data: Variable Selection Methods
Biclustering is a technique for clustering rows and columns of a data matrix simultaneously. Over the past few years, we have seen its applications in biology-related fields, as well as in many data mining projects. As opposed to classical clustering methods, biclustering groups objects that are sim...
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| Language: | en_US |
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Harvard University
2013
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| Online Access: | http://dissertations.umi.com/gsas.harvard:11201 http://nrs.harvard.edu/urn-3:HUL.InstRepos:11181216 |
| Summary: | Biclustering is a technique for clustering rows and columns of a data matrix simultaneously. Over the past few years, we have seen its applications in biology-related fields, as well as in many data mining projects. As opposed to classical clustering methods, biclustering groups objects that are similar only on a subset of variables. Many biclustering algorithms on continuous data have emerged over the last decade. In this dissertation, we will focus on two Bayesian biclustering algorithms we developed for discrete data, more specifically categorical data and ordinal data. === Statistics |
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