Fuzzy Learning of Co-Similarities from Large-Scale Documents

Sonia Alouane-Ksouri and Minyar Sassi Hidri
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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

International Journal of Fuzzy System Applications (IJFSA), 2015, vol. 4, issue 4, 70-86

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jfsa00:v:4:y:2015:i:4:p:70-86

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