Rule Optimization of Web-Logs Data Using Evolutionary Technique

Rule Optimization of Web-Logs Data Using Evolutionary Technique

Manish Kumar, Sumit Kumar
Copyright: © 2015 |Pages: 12
DOI: 10.4018/978-1-4666-7456-1.ch009
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Abstract

Web usage mining can extract useful information from Weblogs to discover user access patterns of Web pages. Web usage mining itself can be classified further depending on the kind of usage data. This may consider Web server data, application server data, or application level data. Web server data corresponds to the user logs that are collected at Web servers. Some of the typical data collected at Web server are the URL requested, the IP address from which the request originated, and timestamp. Weblog data is required to be cleaned, condensed, and transformed in order to retrieve and analyze significant and useful information. This chapter analyzes access frequent patterns by applying the FP-growth algorithm, which is further optimized by using Genetic Algorithm (GA) and fuzzy logic.
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3. Objective

Web contains large amount of incredible information. Though it is tough to deal with vast information with user’s perspective, Web service provider’s perspective and business analyst’s perspective because of its high complexities. Web service providers want to predict the user’s behavior to design the website according to user’s perspective and also to reduce the traffic load. Analysis can be done on the user’s history from weblog patterns to retrieve useful information. This information can be used in different forms and places in e-business, website designing, market campaigns, measuring the success of marketing efforts, customer-company behavior and many more applications.

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