Behavior Study of Web Users Using Two-Phase Utility Mining and Density Based Clustering Algorithms

摘要

With the recent explosive growth of the amount of content online, it has grown to become increasingly difficult for users to obtain and utilize information and for contents services to classify and catalog documents. Traditional web the search engines often return hundreds or thousands of results for a search, and that is time intensive for consumers to browse. Typically, in a data mining process, the number of patterns discovered can easily exceed the capabilities of a human user to identify interesting results. To address this problem, utility measures have been used to reduce the patterns prior to presenting them to the user. A frequent itemset just shows the statistical correlation between items, and it will not reflect the semantic significance of the items. This proposed approach utilizes a utility based itemset mining approach to overcome this limitation. This suggested system initial uses Dbscan clustering algorithm which identifies the behavior of the users page visits, order of occurrence of visits .