Chameleon Clustering Algorithm with Semantic Analysis Algorithm for Efficient Web Usage Mining

Abstract

World Wide Web (WWW) has increasing information, and the websites usage also increasing rapidly. The web server records the users’ search activity on the web and store it in the server log. When these web logs properly mined, lot of useful information can be provided for the decision making. Thus the process of accessing relevant information becomes easier. The web log mining usually used for the purpose of generating a model for search pattern discovery with the semantic meaning. Hence the CHAMELEON clustering algorithm is used for the clustering the user search logs based on the search category. Semantic analysis is performed in addition on the clustered logs to extract the semantically similar patterns. This process of discovering the frequent search pattern based on semantic relation can be used for providing user pertinent information using web usage mining. Therefore, the proposed system brings the frequent patterns as output with semantical information that supports the user to understand the pattern semantically.
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