# Email Surveillance Using Non-negative Matrix Factorization

@article{Berry2005EmailSU, title={Email Surveillance Using Non-negative Matrix Factorization}, author={Michael W. Berry and Murray Browne}, journal={Computational \& Mathematical Organization Theory}, year={2005}, volume={11}, pages={249-264} }

In this study, we apply a non-negative matrix factorization approach for the extraction and detection of concepts or topics from electronic mail messages. For the publicly released Enron electronic mail collection, we encode sparse term-by-message matrices and use a low rank non-negative matrix factorization algorithm to preserve natural data non-negativity and avoid subtractive basis vector and encoding interactions present in techniques such as principal component analysis. Results in topic…

## 174 Citations

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The underlaying mathematical NMF theory is described along with some extensions and several relevant applications from different scientific areas are presented.

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This paper addresses the text clustering problem via a novel strategy, called Pairwise Constraintsguided Non-negative Matrix Factorization (PCNMF), which can capture the available abundance prior constraints in original space, which result in more effective for clustering or information retrieval.

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Non-negative matrix factorization (NMF) is used to analyze the data from stock market and decomposes the data matrix V of the daily closing prices of the 40 stocks into two matrices W and H, in which the columns of W correlate to the underlying trends.

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This paper proposes an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix, and demonstrates that this approach is able to retrieve the hidden structure of data and determine the correct rank of Base matrix.

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