Abstract
The unlabeled document or text collections are becoming larger and larger which is common and obvious; mining such data sets are a challenging task. Using the simple word-document frequency matrix as feature space the mining process is becoming more complex. The text documents are often represented as high dimensional about few thousand sparse vectors with sparsity about 95 to 99% which significantly affects the efficiency and the results of the mining process. In this paper, we propose the two-stage Non-negative Matrix Factorization (NMF): in the first stage we tried to extract the uncorrelated basis probabilistic document feature vectors by significantly reducing the dimension of the feature vectors of the word-document frequency from few thousand to few hundred, and in the second stage for clustering or classification. In our propose approach it has been observed that the clustering or classification performance with more than 98.5% accuracy. The dimension reduction and classification performance has observed for the Classic3 dataset.
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© 2006 Springer-Verlag Berlin Heidelberg
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Barman, P.C., Iqbal, N., Lee, SY. (2006). Non-negative Matrix Factorization Based Text Mining: Feature Extraction and Classification. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_78
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DOI: https://doi.org/10.1007/11893257_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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