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Activity Number: 698
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract - #308888
Title: Bayesian Clustering Using MCMC Sampling
Author(s): Mahroo Vahidpour*+ and Vahid Partovi Nia
Companies: Polytechnique Montréal and École Polytechnique Montréal
Keywords: Bayesian clustering ; Gibbs sampling ; Metropolis-Hasting algorithm ; Split-Merge algorithm
Abstract:

Clustering can be described as the partitioning of data into homogeneous groups or clusters. The modern clustering approaches such as the EM algorithm or the k-means are sensitive to initial values. In order to make the clustering algorithm insensitive to starting values, we consider the data groupings as an unobserved random variable and use stochastic search or sampling from the grouping posterior using MCMC. We demonstrate our methodology on metabolomic data.


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