Representation of textual documents by the approach wordnet and n-grams for the unsupervised classification (clustering) with 2D cellular automata: a comparative study.
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%0 Journal Article
%1 journals/ccsecis/HamouLLR10
%A Hamou, Reda Mohamed
%A Lehireche, Ahmed
%A Lokbani, Ahmed Chaouki
%A Rahmani, Mohamed
%D 2010
%J Computer and Information Science
%K dblp
%N 3
%P 240-255
%T Representation of textual documents by the approach wordnet and n-grams for the unsupervised classification (clustering) with 2D cellular automata: a comparative study.
%U http://dblp.uni-trier.de/db/journals/ccsecis/ccsecis3.html#HamouLLR10
%V 3
@article{journals/ccsecis/HamouLLR10,
added-at = {2019-05-22T00:00:00.000+0200},
author = {Hamou, Reda Mohamed and Lehireche, Ahmed and Lokbani, Ahmed Chaouki and Rahmani, Mohamed},
biburl = {https://www.bibsonomy.org/bibtex/21aedb3edd39bf76c409001a043d7ab48/dblp},
ee = {https://doi.org/10.5539/cis.v3n3p240},
interhash = {4f9a069a53597d44d1195322e094391f},
intrahash = {1aedb3edd39bf76c409001a043d7ab48},
journal = {Computer and Information Science},
keywords = {dblp},
number = 3,
pages = {240-255},
timestamp = {2019-05-23T11:38:33.000+0200},
title = {Representation of textual documents by the approach wordnet and n-grams for the unsupervised classification (clustering) with 2D cellular automata: a comparative study.},
url = {http://dblp.uni-trier.de/db/journals/ccsecis/ccsecis3.html#HamouLLR10},
volume = 3,
year = 2010
}
