文章基本信息

标题：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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作者：HAMOU Reda Mohamed ; LEHIRECHE Ahmed ; LOKBANI Ahmed Chaouki 等
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2010
卷号：3
期号：3
页码：240
DOI：10.5539/cis.v3n3p240
出版社：Canadian Center of Science and Education
摘要：Normal 0 21 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tableau Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} In this article we present a 2D cellular automaton (Class_AC) to solve a problem of text mining in the case of unsupervised classification (clustering). Before to experiment the cellular automaton, we vectorized our data indexing textual documents from the database REUTERS 21,578 by Wordnet approach and the representation of text documents by the method n-grams. Our work is to make a comparative study of two approaches to representation that is the conceptual approach (Wordnet) and the n-grams. Section 1 gives an introduction on the biomimétisme and text mining, Section 2 presents r epresentation of texts based on Wordnet approach and the n grams , Section 3 describes the cellular automaton for clustering, Section 4 shows the experimentation and comparison results and finally Section 5 gives a conclusion and perspectives.