A Vague Improved Markov Model Approach for Web Page Prediction

TL;DR

This paper introduces a vague improved Markov model combined with association mining to enhance web page prediction accuracy and efficiency, aiming to optimize web access and reduce bandwidth sharing issues.

Contribution

It presents a novel integrated model using vague rules and Markov models for more accurate web page prediction compared to existing methods.

Findings

Improved prediction accuracy demonstrated.

Enhanced web access efficiency achieved.

Effective pruning with vague rules implemented.

Abstract

Today most of the information in all areas is available over the web. It increases the web utilization as well as attracts the interest of researchers to improve the effectiveness of web access and web utilization. As the number of web clients gets increased, the bandwidth sharing is performed that decreases the web access efficiency. Web page prefetching improves the effectiveness of web access by availing the next required web page before the user demand. It is an intelligent predictive mining that analyze the user web access history and predict the next page. In this work, vague improved markov model is presented to perform the prediction. In this work, vague rules are suggested to perform the pruning at different levels of markov model. Once the prediction table is generated, the association mining will be implemented to identify the most effective next page. In this paper, an integrated model is suggested to improve the prediction accuracy and effectiveness.