Bayesian Biclustering on Discrete Data
Subtitle: Variable Selection Methods
Contributors: Liu, Jun; Harrington, David P.; Dasgupta, Tirthankar
Published: 2013
Language: eng
Description: 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.
Notes: Keywords: BiClustering, Categorical data, Hapmap, Ordinal data, population structure.

Thesis Ph.D. Harvard University 2013