An Empirical Proposal towards the Algorithmic Approach and Pattern in Web Mining for Assorted Applications
|Harleen Puri1, Arvind Selwal2, Anuradha Sharma3
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Data mining or the analysis phase of the knowledge discovery process is the computational process of discovering patterns in large data sets that involves methods at the intersection of artificial intelligence, machine learning, statistics, and database system. The classical goal of the data mining and machine learning process is to fetch and extract information from a data set and transform it into an understandable structure for further use. Besides raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Web Usage Mining is the type of data mining technique to discover interesting usage patterns from web data, in order to discover useful pattern and better serve the needs of web-based applications. Usage data captures the identity or origin of web users along with their browsing behavior at a web site. Web usage mining itself may be classified further depending on the kind of usage data considered. They are web server data, application server data and application level data. Web server data correspond to the user logs that are collected at web server. Some of the typical data collected and saved at a web server include IP addresses, page references, and access time of the users. In this paper a new technique is proposed to discover the web usage patterns of websites from the server log files with the foundation of clustering and improved Apriori algorithm.