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Abstract
The rapid increase in web sites has created lar~e volume of data in
web environment, which generate a problem of how to extract and gain
useful knowledge from such data. Extracting such knowledge introduces
a rapid services, saves user time with web, and improves user’s efficiency
and effectiveness in searching for information on the web. Also this
knowledge helps to discovering, and predicting user’s behaviors based on
their interaction with a website (Personalization).
In this thesis an overview of approaches for data mining and its
objectives, process and applications would be explained. Web mining
concept (web content mining, web structure mining, web usage mining)
and its applications would be introduced .General framework for fully
stages of web usage mining including (preprocessing, pattern discovery
and pattern analysis), and an approach for e-business and personalization
operation would be discussed. Also an overview for different types of
data sources, and a brief description for web log files structures would be
discussed.
Web muung techniques used for analyzing web data like
Classification ,Clustering, Sequential Patterns, Association Rules ,and
specially Artificial Neural Network behaviors (Operation, Architecture
,Different Training Rules Algorithms and Applications) would be
discussed.
The aim of this thesis is analyzing web log file data for Information
System of Mansoura University Center using proposed Web Knowledge”
Extraction from Logs Files (WKELF) analyzing system .This analysis