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- W2803088253 abstract "Nowa day’s electronic gadgetsare easily available dueto their affordable rates and drastic increase in E- commercesites online shopping business industry reaches sky limits. Somillions of transactions are happening at the E-commerce siteweb servers. And the data dumps at the server logs will be interms of gigabytes or sometimes it is in Terabytes. So this muchhuge data is always plays an important role in identifying missingtransactions or mining most reliable sold items which can behelpful for the E commerce industry. Many algorithms andmethodologies are existed to mine the web log data likeAprioriand others.But most of the algorithms are suffered from spacecomplexity issue with the limited itemsets. So proposed systemintroduces a technique of mining the E-commerce web serverlogs using M- tree based frequent pattern analysis. And thisprocess is powered with the Shannon information gain techniqueto identify the most important items that are distributed over thedatasets." @default.
- W2803088253 created "2018-05-17" @default.
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- W2803088253 date "2017-11-01" @default.
- W2803088253 modified "2023-09-24" @default.
- W2803088253 title "E-Commerce Web Miner Web Mining Algorithm For Web Log Analysis" @default.
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- W2803088253 hasPublicationYear "2017" @default.
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