The non-negative matrix factorization toolbox for biological data mining by Yifeng Li and Alioune Ngom. (Source Code for Biology and Medicine 2013, 8:10 doi:10.1186/1751-0473-8-10)
From the post:
Background: Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data. Though there currently exists packages implemented in R and other programming languages, they either provide only a few optimization algorithms or focus on a specific application field. There does not exist a complete NMF package for the bioinformatics community, and in order to perform various data mining tasks on biological data.
Results: We provide a convenient MATLAB toolbox containing both the implementations of various NMF techniques and a variety of NMF-based data mining approaches for analyzing biological data. Data mining approaches implemented within the toolbox include data clustering and bi-clustering, feature extraction and selection, sample classification, missing values imputation, data visualization, and statistical comparison.
Conclusions: A series of analysis such as molecular pattern discovery, biological process identification, dimension reduction, disease prediction, visualization, and statistical comparison can be performed using this toolbox.
Written in a bioinformatics context but also used in text data mining (Enron emails), spectral analysis and other data mining fields. (See Non-negative matrix factorization)