Analysis of Users’ Web Navigation Behavior using GRPA with Variable Length Markov Chains

Madhuri, Ch Bindu; Chandulal, J Anand; Ramya, K.; Phanidra, M

doi:10.5121/ijdkp.2011.1201

Cited by 14 publications

(8 citation statements)

References 39 publications

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“…However, the large number of states in the model degrades the performance of prediction of next web page accessed by the users. An approach was proposed [12] to analysis the navigational behavior of user. The proposed approach used Grey Relation Pattern Analysis (GRPA) with variable length Markov Chain.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

Rooba*

2020

IJRTE

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Semantic Variable Length Markov Chain Length Model (SVLMC) is a web page recommendation system which combined the fields of semantic web and web usage mining by the Markov transition probability matrix with rich semantic information extracted from web pages. Though it has high prediction accuracy, it has problem of high state space complexity. The high space complexity reduce the execution speed and reduce the performance of the system, which was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) model that provides high user satisfied recommendation and Confidence Pruned Markov Model (CPMM). The time consumption of CPMM was reduced by Support Vector Machine (SVM). But still the recommendation accuracy is still below the user satisfaction. So in this paper, quickest change detection using Kullback-Leibler Divergence method is introduced to improve the accuracy of recommendation generation by developing a scalable quickest change detection schemes that can be implemented recursively in a more complicated scenario of Markov model and it is included in the training data of SVM. Then the performance of web page recommendation is improved by ranking the web pages using page ranking technique. Thus the performance of web page recommendation generation system has been improved. The experiments are conducted to prove the effectiveness of the proposed work in terms of prediction accuracy, precision, recall, F1-measure, coverage and R measure.

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Section: Literature Surveymentioning

confidence: 99%

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

Rooba*

2020

IJRTE

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“…Bindu Madhuri et al, in [6] has proposed a method to analyse the navigational behaviour of users using GRPA (Grey Relational Pattern Analysis) and VLMC (Variable Length Markov Chains).…”

Section: Related Workmentioning

confidence: 99%

Web page access prediction using hierarchical clustering based on modified levenshtein distance and higher order Markov model

Kumar¹,

Vibha²,

Venugopal³

2016

2016 IEEE Region 10 Symposium (TENSYMP)

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Abstract---Web Page access prediction is a challenging task in the current scenario, which draws the attention of many researchers. Predictions need to keep track of history data to analyze the usage behavior of the users. Web Usage behavior of a user can be analyzed using the web log file of a specific website. User behavior can be analyzed by observing the navigation patterns. This approach requires user session identification, clustering the sessions into similar clusters and developing a model for prediction using the current and earlier accesses. Most of the previous works in this field have used K-Means clustering technique with Euclidean distance for computation. The drawbacks of K-Means is that deciding on the number of clusters, choosing the initial random center are difficult and the order of page visits are not considered. The proposed research work uses hierarchical clustering technique with modified Levenshtein distance, Page Rank using access time length, frequency and higher order Markov model for prediction. Experimental results prove that the proposed approach for prediction gives better accuracy over the existing techniques.

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“…State C is not accurately showing his actual probability. The by separating the in paths to it [10]. For solve this accuracy problem state cloning is required or need to find out another improved method for accuracy increasing.…”

Section: Advance Modelmentioning

confidence: 99%

Effective Cleaning of Educational Website Usage Patterns and Predicting their Next Visit

Kumar¹,

Solanki²

2012

IJCA

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Universities with web education rely on web usage analysis to obtain students behavior for education promotion. The Internet is an information gateway and as a medium for business and education industry. Finding hidden information from Web log data is called Web usage mining. The aim of discovering similar patterns in Web log data is to obtain information about the navigational behavior of the users. Web usage mining, from the data mining aspect, is the task of applying data mining techniques to discover usage patterns from Web data in order to understand and better serve the needs of users navigating on the Web. Web usage mining aim is to find out useful information from the educational weblogs. These useful data pattern are used to analyze behavior of user. The focus of this paper is to generate a cleaning algorithm and provide an overview how to use frequent pattern mining techniques for discovering different types of patterns in a Web log. In this paper we premeditated different pattern for web pages, general statics, activity statics,

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Analysis of Users’ Web Navigation Behavior using GRPA with Variable Length Markov Chains

Abstract: With the never-ending growth of Web services and Web-based

Cited by 14 publications

References 39 publications

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

Web page access prediction using hierarchical clustering based on modified levenshtein distance and higher order Markov model

Effective Cleaning of Educational Website Usage Patterns and Predicting their Next Visit

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