EMCC: Evolutionary Monte Carlo (EMC) Methods for Clustering

Evolutionary Monte Carlo methods for clustering, temperature ladder construction and placement. This package implements methods introduced in Goswami, Liu and Wong (2007) <doi:10.1198/106186007X255072>. The paper above introduced probabilistic genetic-algorithm-style crossover moves for clustering. The paper applied the algorithm to several clustering problems including Bernoulli clustering, biological sequence motif clustering, BIC based variable selection, mixture of Normals clustering, and showed that the proposed algorithm performed better both as a sampler and as a stochastic optimizer than the existing tools, namely, Gibbs sampling, ``split-merge'' Metropolis-Hastings algorithm, K-means clustering, and the MCLUST algorithm (in the package 'mclust').

Getting started

Package details

AuthorGopi Goswami <[email protected]>
MaintainerGopi Goswami <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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EMCC documentation built on May 29, 2017, 1:03 p.m.