Modeling a generic web classification system using design patterns

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Publication Details

Author list: Sukakanya U., Porkaew K.

Publication year: 2011

Volume number: 6

Issue number: 10

Start page: 2212

End page: 2220

Number of pages: 9

ISSN: 1796-203X

eISSN: 1796-203X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053328158&doi=10.4304%2fjcp.6.10.2212-2220&partnerID=40&md5=e6848928351836ba0d3793dbf4036d90

Languages: English-Great Britain (EN-GB)

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Abstract

In order to save time in extracting specific information from high volume of data in web documents, this paper proposes an architectural model of generic web document classification system using design patterns for classifying web documents. This work implements two classification techniques for classifying Thai web documents, namely centroid classification and neural network classification, based on the proposed model and compares their classification effectiveness empirically. The training data sets in this experiment consist of 500 web documents of the following five categories (100 documents for each category): mobile phone sales, book sales, travel sales, education information and company profile. Another two hundred and fifty web documents were then used to test the two classifiers. The experiment results showed that the centroid classifier outperforms the neural network classifier both in term of efficiency and effectiveness. ฉ 2011 ACADEMY PUBLISHER.

Keywords

Centroid, Document analyzer, text classification, Web classification modeling