With the explosive growth of information sources available on the World Wide Web, it has become increasingly necessary for users to utilize automated tools in find the desired information resources, and to track and analyze their usage patterns. These factors give rise to the necessity of creating serverside and clientside intelligent systems that can effectively mine for knowledge. Web mining can be broadly defined as the discovery and analysis of useful information from the World Wide Web. This describes the automatic search of information resources available online, i.e. Web content mining, and the discovery of user access patterns from Web servers, i.e., Web usage mining.
Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the WorldWide Web. There are roughly three knowledge discovery domains that pertain to web mining: Web Content Mining, Web Structure Mining, and Web Usage Mining. Web content mining is the process of extracting knowledge from the content of documents or their descriptions. Web document text mining, resource discovery based on concepts indexing or agentbased technology may also fall in this category. Web structure mining is the process of inferring knowledge from the WorldWide Web organization and links between references and referents in the Web. Finally, web usage mining, also known as Web Log Mining, is the process of extracting interesting patterns in web access logs.
- Web Content Mining
Web content mining is an automatic process that goes beyond keyword extraction. Since the content of a text document presents no machinereadable semantic, some approaches have suggested to restructure the document content in a representation that could be exploited by machines. The usual approach to exploit known structure in documents is to use wrappers to map documents to some data model. Techniques using lexicons for content interpretation are yet to come.
There are two groups of web content mining strategies: Those that directly mine the content of documents and those that improve on the content search of other tools like search engines.
- Web Structure Mining
WorldWide Web can reveal more information than just the information contained in documents. For example, links pointing to a document indicate the popularity of the document, while links coming out of a document indicate the richness or perhaps the variety of topics covered in the document. This can be compared to bibliographical citations. When a paper is cited often, it ought to be important. The PageRank and CLEVER methods take advantage of this information conveyed by the links to find pertinent web pages. By means of counters, higher levels cumulate the number of artifacts subsumed by the concepts they hold. Counters of hyperlinks, in and out documents, retrace the structure of the web artifacts summarized. - Web Usage Mining
Web servers record and accumulate data about user interactions whenever requests for resources are received. Analyzing the web access logs of di#erent web sites
can help understand the user behaviour and the web structure, thereby improving the design of this colossal collection of resources. There are two main tendencies in Web Usage Mining driven by the applications of the discoveries: General Access Pattern Tracking and Customized Usage Tracking.
The general access pattern tracking analyzes the web logs to understand access patterns and trends. These analyses can shed light on better structure and grouping of resource providers. Many web analysis tools existd but they are limited and usually unsatisfactory. We have designed a web log data mining tool, WebLogMiner, and proposed techniques for using data mining and OnLine Analytical Processing (OLAP) on treated and transformed web access files. Applying data mining techniques on access logs unveils interesting access patterns that can be used to restructure sites in a more efficient grouping, pinpoint effective advertising locations, and target specific users for specific selling ads.
Customized usage tracking analyzes individual trends. Its purpose is to customize web sites to users. The information displayed, the depth of the site structure and the format of the resources can all be dynamically customized for each user over time based on their access patterns.
While it is encouraging and exciting to see the various potential applications of web log file analysis, it is important to know that the success of such applications depends on what and how much valid and reliable knowledge one can discover from the large raw log data. Current web servers store limited information about the accesses. Some scripts customtailored for some sites may store additional information. However, for an effective web usage mining, an important cleaning and data transformation step before analysis may be needed.
