A great number of individuals are anxious to exploit the internet`s wealth of information. It can be employed to further enhance the existing data. However, the primary challenge lies in uncovering the valuable information that is concealed within HTML elements. This study proposes a framework for web usage mining that examines web server log files using sequential pattern mining approaches. Web log patterns reveal information about user behavior, preferences, and website interactions. Preprocessing of the web data was carried out. The primary objective of preprocessing is to enhance data integrity while decreasing the volume of information that requires evaluation. Prior to inputting the data into the pattern discovery phase, it is necessary to eliminate noise by resolving the challenge of distinguishing between different users and sessions. To identify frequent sequential access in large, low-support data sets, a method for mining sequential patterns is developed. A sequential pattern mining technique identifies recurring sequential patterns in multidimensional web log files with minimum support provided. Multidimensional sequential pattern mining is primarily concerned with enhancing the standard of the patterns the user received back. PrefixSpan algorithm has been used to extract tabular as well as unstructured data from HTML tag. Prefix prunes some web info by calculating the support value at different nodes in the represented projected sub-database and snipe away huge portions of the representation that are guaranteed not to create any outcomes. The system is implemented in Matlab programming language. In the domain of web mining, Matlab has been employed to extract valuable information from the web, including user records and content. When mining extensive sequences containing numerous records, in particular, the method substantially reduces execution time and eliminates enormous memory access costs. The PrefixSpan algorithm enhanced with the starting position and innertagcount parameters has better performance than Markov model and GSP algorithm with execution time of 2.35seconds.

Web Usage Mining, Sequential Patterns, Web Access Pattern, Prefixspan, Web Server logs, Preprocessing

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