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Volume 4 Issue 11
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E-Commerce Web Miner Web Mining Algorithm For Web Log Analysis

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Nowa day’s electronic gadgetsare easily available due to their affordable rates and drastic increase in E- commerce sites online shopping business industry reaches sky limits. So millions of transactions are happening at the E-commerce site web servers. And the data dumps at the server logs will be in terms of gigabytes or sometimes it is in Terabytes. So this much huge data is always plays an important role in identifying missing transactions or mining most reliable sold items which can be helpful for the E commerce industry. Many algorithms and methodologies are existed to mine the web log data likeApriori and others.But most of the algorithms are suffered from space complexity issue with the limited itemsets. So proposed system introduces a technique of mining the E-commerce web server logs using M- tree based frequent pattern analysis. And this process is powered with the Shannon information gain technique to identify the most important items that are distributed over the datasets.

Key Words

Shannon information gain, M- tree, Web logs, Pre order traversing , Support clustering

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"E-Commerce Web Miner Web Mining Algorithm For Web Log Analysis", International Journal of Emerging Technologies and Innovative Research ( | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 11, page no. pp1-6, November-2017, Available at : &

Publication Details

Published Paper ID: JETIR1711001
Registration ID: 170474
Published In: Volume 4 | Issue 11 | Year November-2017
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Page No: 1-6
ISSN Number: 2349-5162

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(Volume 5 | Issue 1 | January 2018 |Impact factor 5.87)

Call For Paper | Volume 5 | Issue 1 | Impact factor 5.87

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