Document clustering using nonnegative matrix factorization

A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies. Key words: conjugate gradient, constrained least squares, nonnegative matrix factorization, text mining.

• Type: Journal
• Year: 2006
• Where: IPM
• Authors: Farial Shahnaz, Michael W. Berry, V. Paul Pauca, Robert J. Plemmons
• Keywords: IPM 2006, Nonnegative Matrix Factorization, Rank Nonnegative Matrix, Semantic Feature