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Fuzzy Learning of Co-Similarities from Large-Scale Documents

Author

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  • Sonia Alouane-Ksouri

    (Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunis, Tunisia)

  • Minyar Sassi Hidri

    (Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunis, Tunisia)

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.

Suggested Citation

  • Sonia Alouane-Ksouri & Minyar Sassi Hidri, 2015. "Fuzzy Learning of Co-Similarities from Large-Scale Documents," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 4(4), pages 70-86, October.
    • Handle: RePEc:igg:jfsa00:v:4:y:2015:i:4:p:70-86
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