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- W3100445095 abstract "The volume of texts produced daily is growing significantly. Computers have difficulty processing and interpreting this considerable amount of largely unstructured data without using feasible techniques. Efficient and effective techniques and algorithms are important in exploring useful patterns. Text mining (TM), the process of extracting useful knowledge from text, is gaining considerable research attention. In this article, we explain the natural gradient independent component analysis (NGICA) of text classification (TC), which is an application of TM involving the preprocessing of text data followed by classification. An unsupervised framework of machine-learning classification is proposed based on NGICA for TC. A manually labelled dataset is used to assess the proposed model, and its validity is proven through experiments. The proposed model achieves an overall F-measure of 83% for TC. Results show the potential of the proposed model for Compared with the infomax method that provided 81% of an overall F-measure for TC." @default.
- W3100445095 created "2020-11-23" @default.
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- W3100445095 date "2020-07-12" @default.
- W3100445095 modified "2023-10-16" @default.
- W3100445095 title "Independent Component Analysis Based on Natural Gradient Algorithm for Text Mining" @default.
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- W3100445095 doi "https://doi.org/10.1109/it-ela50150.2020.9253072" @default.
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