Abstract
Non-negative tensor factorization (NTF) is a technique that has been used effectively for the purposes of analyzing large textual datasets. This article describes the improvements achieved by creating a Python implementation of the NTF algorithm, and by integrating it with several pre-processing and post-processing functions within a single Python-based analysis environment. The improved implementation allows the user to construct and modify the contents of the tensor, experiment with relative term weights and trust measures, and experiment with the total number of algorithm output features. Non-negative tensor factorization output feature production is closely integrated with a visual post-processing tool, FutureLens, that allows the user to perform in-depth analysis of textual data, facilitating scenario extraction and knowledge discovery.
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© 2012 Springer-Verlag London Limited
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Puretskiy, A.A., Berry, M.W. (2012). Knowledge Discovery Using Nonnegative Tensor Factorization with Visual Analytics. In: Berry, M., et al. High-Performance Scientific Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2437-5_17
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DOI: https://doi.org/10.1007/978-1-4471-2437-5_17
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2436-8
Online ISBN: 978-1-4471-2437-5
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