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Fuzzy Learning of Co-Similarities from Large-Scale Documents

Fuzzy Learning of Co-Similarities from Large-Scale Documents

Sonia Alouane-Ksouri, Minyar Sassi Hidri
Copyright: © 2015 |Volume: 4 |Issue: 4 |Pages: 17
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781466679986|DOI: 10.4018/ijfsa.2015100104
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MLA

Alouane-Ksouri, Sonia, and Minyar Sassi Hidri. "Fuzzy Learning of Co-Similarities from Large-Scale Documents." IJFSA vol.4, no.4 2015: pp.70-86. http://doi.org/10.4018/ijfsa.2015100104

APA

Alouane-Ksouri, S. & Hidri, M. S. (2015). Fuzzy Learning of Co-Similarities from Large-Scale Documents. International Journal of Fuzzy System Applications (IJFSA), 4(4), 70-86. http://doi.org/10.4018/ijfsa.2015100104

Chicago

Alouane-Ksouri, Sonia, and Minyar Sassi Hidri. "Fuzzy Learning of Co-Similarities from Large-Scale Documents," International Journal of Fuzzy System Applications (IJFSA) 4, no.4: 70-86. http://doi.org/10.4018/ijfsa.2015100104

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Abstract

To analyze and explore large textual corpus, we are generally limited by the available main memory. This may lead to a proliferation of processor load due to greedy computing. The authors propose to deal with this problem to compute co-similarities from large-scale documents. The authors propose to enhance co-similarity learning by upstream and downstream parallel computing. The first deploys the fuzzy linear model in a Grid environment. The second deals with multi-view datasets while introducing different architectures by using several instances of a fuzzy triadic similarity algorithm.

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