Web Page Classification Using Distributed Learning Automata and Partitioning Graph Algorithm

Mahdi Bazarganigilani; Ali Syed

doi:10.1109/DEXA.2010.66

Web Page Classification Using Distributed Learning Automata and Partitioning Graph Algorithm

Mahdi Bazarganigilani, Ali Syed

Computing, Mathematics and Engineering

Research output: Book chapter/Published conference paper › Conference paper › peer-review

2 Citations (Scopus)

Abstract

The characteristic of dynamic websites is that they include hidden contents, and this huge repository is only accessible via the website interfaces. This is a vital capability of all search engines, thus providing the users with links that are more relevant and ranked according to their needs. The drawback of most search engine algorithms is that they rank pages based on hyperlinked relative importance to other pages, rather than user intent and interest. This paper proposes a method based on Learning Automata for the classification of the webpage searches.

Original language	English
Title of host publication	Focused Web Crawling Using Decay Concept and Genetic Programming
Publisher	IEEE
Pages	302-304
Number of pages	3
ISBN (Electronic)	9780769541747
DOIs	https://doi.org/10.1109/DEXA.2010.66
Publication status	Published - 2010
Event	Workshops on Database and Expert Systems Applications - Bilbao, Spain, Spain Duration: 30 Aug 2010 → 03 Sept 2010

Workshop

Workshop	Workshops on Database and Expert Systems Applications
Country/Territory	Spain
Period	30/08/10 → 03/09/10

Access to Document

10.1109/DEXA.2010.66

Cite this

@inproceedings{130db936eb0444f384e9a5c2d14cc53e,

title = "Web Page Classification Using Distributed Learning Automata and Partitioning Graph Algorithm",

abstract = "The characteristic of dynamic websites is that they include hidden contents, and this huge repository is only accessible via the website interfaces. This is a vital capability of all search engines, thus providing the users with links that are more relevant and ranked according to their needs. The drawback of most search engine algorithms is that they rank pages based on hyperlinked relative importance to other pages, rather than user intent and interest. This paper proposes a method based on Learning Automata for the classification of the webpage searches.",

keywords = "Component, Distributed Learning Automata, Graph Partitioning Algorithm, Web classification, Web page ranking",

author = "Mahdi Bazarganigilani and Ali Syed",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = IEEE, 2010. Event dates (773o) = 30-08-2010-03-09-2010; Parent title (773t) = Workshops on Database and Expert Systems Applications.; Workshops on Database and Expert Systems Applications ; Conference date: 30-08-2010 Through 03-09-2010",

year = "2010",

doi = "10.1109/DEXA.2010.66",

language = "English",

pages = "302--304",

booktitle = "Focused Web Crawling Using Decay Concept and Genetic Programming",

publisher = "IEEE",

}

TY - GEN

T1 - Web Page Classification Using Distributed Learning Automata and Partitioning Graph Algorithm

AU - Bazarganigilani, Mahdi

AU - Syed, Ali

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = IEEE, 2010. Event dates (773o) = 30-08-2010-03-09-2010; Parent title (773t) = Workshops on Database and Expert Systems Applications.

PY - 2010

Y1 - 2010

N2 - The characteristic of dynamic websites is that they include hidden contents, and this huge repository is only accessible via the website interfaces. This is a vital capability of all search engines, thus providing the users with links that are more relevant and ranked according to their needs. The drawback of most search engine algorithms is that they rank pages based on hyperlinked relative importance to other pages, rather than user intent and interest. This paper proposes a method based on Learning Automata for the classification of the webpage searches.

AB - The characteristic of dynamic websites is that they include hidden contents, and this huge repository is only accessible via the website interfaces. This is a vital capability of all search engines, thus providing the users with links that are more relevant and ranked according to their needs. The drawback of most search engine algorithms is that they rank pages based on hyperlinked relative importance to other pages, rather than user intent and interest. This paper proposes a method based on Learning Automata for the classification of the webpage searches.

KW - Component

KW - Distributed Learning Automata

KW - Graph Partitioning Algorithm

KW - Web classification

KW - Web page ranking

U2 - 10.1109/DEXA.2010.66

DO - 10.1109/DEXA.2010.66

M3 - Conference paper

SP - 302

EP - 304

BT - Focused Web Crawling Using Decay Concept and Genetic Programming

PB - IEEE

T2 - Workshops on Database and Expert Systems Applications

Y2 - 30 August 2010 through 3 September 2010

ER -

Web Page Classification Using Distributed Learning Automata and Partitioning Graph Algorithm

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