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Enhanced Model of Web Page Prediction using Page Rank and Markov Model

by Soumen Swarnakar, Anjali Thakur, Debapriya Misra, Debopriya Paul, Moutrisha Pakira, Sreyashi Roy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 7
Year of Publication: 2016
Authors: Soumen Swarnakar, Anjali Thakur, Debapriya Misra, Debopriya Paul, Moutrisha Pakira, Sreyashi Roy
10.5120/ijca2016909410

Soumen Swarnakar, Anjali Thakur, Debapriya Misra, Debopriya Paul, Moutrisha Pakira, Sreyashi Roy . Enhanced Model of Web Page Prediction using Page Rank and Markov Model. International Journal of Computer Applications. 140, 7 ( April 2016), 30-34. DOI=10.5120/ijca2016909410

@article{ 10.5120/ijca2016909410,
author = { Soumen Swarnakar, Anjali Thakur, Debapriya Misra, Debopriya Paul, Moutrisha Pakira, Sreyashi Roy },
title = { Enhanced Model of Web Page Prediction using Page Rank and Markov Model },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 7 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number7/24609-2016909410/ },
doi = { 10.5120/ijca2016909410 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:41.105405+05:30
%A Soumen Swarnakar
%A Anjali Thakur
%A Debapriya Misra
%A Debopriya Paul
%A Moutrisha Pakira
%A Sreyashi Roy
%T Enhanced Model of Web Page Prediction using Page Rank and Markov Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 7
%P 30-34
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now-a-days, with the massive size of the web it has become hectic as well as time-consuming for the users to search for the most appropriate page in least time. Prediction of the next page saves users’ time and it becomes easy for the user to reach the most suitable or correct page. In this paper, web page prediction technique has been improved by combining clustering with markov rule and page ranking algorithm. The K-means clustering technique is used for the accumulation of the similar web pages. Page Rank Algorithm is used here to assign probabilities to web-pages from beforehand according to their importance. Markov rule has been used on each cluster to evaluate occurrences of each web pages visited under different sessions and markov model is applied to predict the next web page from the current web page. The rule of transition probability of markov model has been used to predict the next web page from the current web page.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Web page prediction Markov Model K-means clustering algorithm Page Rank algorithm Transition probability.

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