Abstract
Web log files contain visitor activity information on a website. They are automatically created and maintained by a web server. The raw web log file format is essentially one line of text for each visit of the web site, each time a visitor requests a page (HTML document, image, etc.), information about the request (including client IP address, request date/time, page requested, HTTP code, bytes served, user agent, and referrer) are added to the current log file. The web maintains a standard format the CLF (Common Log Format) for web server log files. Analysis of server log file can provide significant and useful information, data extracted from log files could be stored in a database, allowing various uses when needed. Data Mining is the process of extracting and discovering patterns and knowledge from large amounts of data. Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the Web. Web Usage Mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. It is the application of data mining techniques to discover usage patterns from web data.
In this paper, we present an analysis and extraction of useful information and patterns from web data based on a deep analysis of web log files, first we start with the presentation of different formats of web log files, then we present the different preprocessing used methods, finally the demonstration of the system “Web content and Usage Mining’’ for extracting knowledge from web data and web site analysis using several Data Mining Algorithms Apriori, FPGrowth, K-Means, KNN, and ID3.
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El Alami, A.A., Ezzikouri, H., Erritali, M. (2020). Data Mining Algorithms for Knowledge Extraction from Web Log Files. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-030-36653-7_12
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