File Exchange

image thumbnail

EM for Mixture of Bernoulli (Unsupervised Naive Bayes) for clustering binary data

version 1.0.0.0 (2.2 KB) by Mo Chen
EM for Mixture of Bernoulli (Unsupervised Naive Bayes) for clustering binary data

8 Downloads

Updated 09 Mar 2016

View License

Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model.
GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable.

EM for Mixture of Bernoulli can be also viewed as an unsupervised version of Naive Bayes classifier, where the M step is Naive Bayes training and E step is Naive Bayes prediction.

This package is now a part of the PRML toolbox (http://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox).

Cite As

Mo Chen (2019). EM for Mixture of Bernoulli (Unsupervised Naive Bayes) for clustering binary data (https://www.mathworks.com/matlabcentral/fileexchange/55882-em-for-mixture-of-bernoulli-unsupervised-naive-bayes-for-clustering-binary-data), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (1)

Ikko

MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux