Web usage Mining is a kind of the web analysis, pre-processing stage in WUM consists the following Data Cleaning, user identification, session identification and path completion (path added). In this research article focuses the path completion part. Web log is the most valuable input of the web analysis with WUM. Web log information is collected from the server, client and proxy server; in these missed some of the information's. It results missing access references, user access patterns are not clearly identified by incomplete access log. To rectify these issues path completion takes the role to acquire the missing reference. Different approaches are available to find the missing reference like Url, IP address, reference length but no one can be efficient. But our proposed candidate key constrained clustering algorithm for path completion can utilize the both type web log file client and server log file and also provide the better results in efficiency rather than the other path completion and construction techniques.
The Web offers several opportunities in the field of education. With the immense expansion of information available on the web, web mining has become appropriate for the web based learning systems. Learning through Online is one of the practical modes of education. Learning Websites, essential courses, Web supported instructional shells, and E-books are some of the modes of delivering the Online Learning. Web mining is the series of task used for mining or extracting useful information from the web pages or web sites. It provides intrinsic knowledge of teaching and learning process for effective education planning by applying various techniques/tools. This paper discusses the benefits and usefulness of web usage mining methods in online learning environment.
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