The Automatic Non-Negative Matrix Factorization of the Hierarchy Clustering Method

Tie Qi Li; Wen Shuo Zhang

doi:10.4028/www.scientific.net/AMM.325-326.1489

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 325-326The Automatic Non-Negative Matrix Factorization of...

The Automatic Non-Negative Matrix Factorization of the Hierarchy Clustering Method

Abstract:

People in such huge information how to find useful information becomes a problem. In order to deal with hierarchical relations in text data, a novel method, called automatic non-negative matrix factorization of the hierarchy clustering, is proposed for the text mining. We use the vector space model as the research foundation, mainly discusses the feature selection and weight calculation two problems. The experimental results on the real data sets demonstrate that our method outperforms, on average, all the other 6 methods.

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Periodical:

Applied Mechanics and Materials (Volumes 325-326)

Pages:

1489-1492

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.325-326.1489

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Online since:

June 2013

Authors:

Tie Qi Li, Wen Shuo Zhang

Keywords:

Association Rule, Clustering Method, Non-Negative Matrix Factorization

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