Recent and Frequent Informative Pages from Web Logs by Weighted Association Rule Mining

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SP. Malarvizhi 1,*

1. Sri Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India

* Corresponding author.


Received: 17 Jul. 2019 / Revised: 13 Aug. 2019 / Accepted: 26 Aug. 2019 / Published: 8 Oct. 2019

Index Terms

Web logs, Web Mining, Page Weight Estimation, Weighted Minimum Support, WARM, WWT


Web Usage Mining provides efficient ways of mining the web logs for knowing the user’s behavioral patterns. Existing literature have discussed about mining frequent pages of web logs by different means. Instead of mining all the frequently visited pages, if the criterion for mining frequent pages is based on a weighted setting then the compilation time and storage space would reduce. Hence in the proposed work, mining is performed by assigning weights to web pages based on two criteria. One is the time dwelled by a visitor on a particular page and the other is based on recent access of those pages. The proposed Weighted Window Tree (WWT) method performs Weighted Association Rule mining (WARM) for discovering the recently accessed frequent pages from web logs where the user has dwelled for more time and hence proves that these pages are more informative. WARM’s significance is in page weight assignment for targeting essential pages which has an advantage of mining lesser quality rules.

Cite This Paper

SP. Malarvizhi, " Recent and Frequent Informative Pages from Web Logs by Weighted Association Rule Mining", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.10, pp. 41-46, 2019. DOI:10.5815/ijmecs.2019.10.05


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