Prediction of users webpage access behaviour using association rule mining

GeethaRamani, R.; Revathy, P.; Jacob, Shomona Gracia

doi:10.1007/s12046-015-0424-0

Cited by 17 publications

(12 citation statements)

References 25 publications

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“…Whereas, multiple user request was difficult to handle in this system. R. Geetharamani, P. Revathy, and S.G. Jacob [12] proposed a new model named as Apriori Prefix Tree (APT) algorithm for web page prediction. The objective of this literature was to predict the probable subsequent page in the web pages based on their navigating behaviour.…”

Section: Literature Reviewmentioning

confidence: 99%

Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

Sahoo¹,

Ratha²

2018

IJIES

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Web page recommendation systems are used to recommend the future web page views to World Wide Web (WWW) users. Existing personalized web page recommendation systems are still limited to several problems such as, cold start, sparse data structures (Sparsity), and no diversity in the set of recommended items (Content Overspecialization). In order to overcome these difficulties, we proposed a new personalized web recommendation system by considering the different contributions of the training samples (MSNBC dataset, and castings technology international dataset). At first, the web pages are converted into number of sequences. An effective clustering algorithm called as Possibilistic Fuzzy C-Means (PFCM) is employed to identify the neighbourhood of each user profile. Then the association rule mining technique, Rapid Frequent Pattern Growth (RFPG) algorithm is used for mining the frequent web pages. Finally, the experimental outcome shows that the proposed approach delivers the high priority of web pages and also recommends the related web pages. Finally, the experimental outcome shows that the proposed approach improved accuracy in web page recommendation up to 50-60% compared to the existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

Sahoo¹,

Ratha²

2018

IJIES

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“…The method doesn't classify all the web pages based on some key features or any specific link towards each web page. R. Geetharamani, P. Revathy, and S.G. Jacob [15], focus on predicting the forecasting the web pages based on the user history of web pages enlisted in their database based on their usage behavior using Apriori Prefix Tree (APT) algorithm. The rules generated based on these algorithms was ranked based on their support, confidence and lift evaluation measures.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proficient Web Recommender System using Hybrid Possiblistic Fuzzy Clustering and Bayesian Model Approach

Venugopal¹,

Nagraj²

2018

IJIES

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In day to day life, the process of projecting the correct information to the authorized person is more difficult, which makes complexity to the decision making process. Web Page Recommendation Systems (WPRS) used in various fields to identify the customer needs and to help the users to take appropriate decisions over the service or product according to his/her preference. The group of users with similar preference will be identified by using Possibilistic Fuzzy C-Means (PFCM) algorithm with an S3I Similarity Measure (SM). The proposed method will determine the gain and loss of the web users based on the web directories which can be modified by using Relevance Feedback Bayesian Network (RFBN) technique. The experimental results are conducted on the MNSBC dataset and the outcomes are compared with the existing methods like Singular Value Decomposition (SVD) methods. The method predicts the accuracy up to 85% when compared with the existing methods and the outcome results proved the effectiveness of the PFCM -RFBN method.

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“…In 2013 R GEETHARAMANI et al [12], proposed Web Usage mining is a technique used to recognize the user needs from the web log. Discovering hidden patterns from the logs is an approaching research area.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Association Rule Mining- A Survey

Pardeshi¹,

Patil²

2017

IJSRCSE

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Abstract-The World Wide Web has become a huge repository of the hypertexts and documents. The rapid growth of web logs and texts has gained a lot of attention from the researchers for extracting the interesting rule for designing of the web pages, drawing the customer preference, analysing the customer behaviour and decision making for serving the organizations with better services. Such decisions are made by analysing different web parameters such as the server log, registration information, access time, session period, page hits and other relative information left by user. This paper presents a survey on various techniques such as fuzzy logic and rule mining for finding the customer behaviour that helps in better decision making and enhancing the performance of the system.

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Prediction of users webpage access behaviour using association rule mining

Cited by 17 publications

References 25 publications

Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

Rapid Frequent Pattern Growth and Possibilistic Fuzzy C-means Algorithms for Improving the User Profiling Personalized Web Page Recommendation System

A Proficient Web Recommender System using Hybrid Possiblistic Fuzzy Clustering and Bayesian Model Approach

Fuzzy Association Rule Mining- A Survey

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