Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining

Title

Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining

Creator

Raman G.; Raj G.K.

Description

Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user's behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIP-BSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIP-BSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods. 2020. All Rights Reserved.

Source

International Journal of Intelligent Engineering and Systems, Vol-14, No. 1, pp. 244-256.

Date

2021-01-01

Publisher

Intelligent Network and Systems Society

Subject

Broken-stick linear regression analysis; Frequent access; Mutual information based pre-processing; Usage behaviour; Web patterns; Web user.

Coverage

Raman G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India; Raj G.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India

Rights

All Open Access; Bronze Open Access

Relation

ISSN: 2185310X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.22266/IJIES2021.0228.24

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099564117&doi=10.22266%2fIJIES2021.0228.24&partnerID=40&md5=58be3865df9371db9349ce708ee1518d