Date of Submission

Spring 2011

Academic Program

Computer Science


Sven Anderson, Rebecca Thomas

Abstract/Artist's Statement

In this project documents that come from defined classes are clustered. The clustering is done using non-negative matrix factorization performed by a approximation method called rank one residue iterations. In order to employ this method the optimal number of clusters and cluster sparsity has to be determined. Normalized mutual information is a measure of how well the clustering represents the original class structure, and this measure is used to find the optimal number of clusters and sparsity.

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