Improved Apriori Algorithm for Web Log Data

Conference: Recent Trends in Information Processing, Computing, Electrical and Electronics
Author(s): Santosh Shakya, Susheel Gupta, Gopal Patidar Year: 2017
Grenze ID: 02.IPCEE.2017.1.16 Page: 85-90

Abstract

Web log mining technique is the very useful technique of extracting useful information from server logs that is use\nWeb usage mining is the process of finding out what users are looking for on the Internet. The mining frequent patterns from\nweb log data can help to optimize the structure of a web site and improve the performance of web servers. Users can also\nbenefit from these frequent patterns from Web. Many efforts have been done to mine frequent patterns efficiently. Apriori\nand its variants and pattern-growth approach are the two representative frequent pattern mining approaches for candidategeneration-\nand-test approach. In this article we have conducted extensive experiments on real world web log data to analyze\nthe characteristics of web logs and the behaviors of these two approaches on web log data. In this paper we propose a new\nApriori algorithm for Frequent Pattern Mining for web log data. Our experimental results show that proposed algorithm can\nsignificantly improve the performance on frequent pattern mining on web log data.

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IPCEE - 2017