A Novel Frequent Pattern Mining Technique for Prediction of User Behavior on Web Stream Data

Abstract

In recent years, as the size of the online databases increases, content in the web pages also increases, then the human behavior towards the online content has become a major issue for decision making. Hence, the need for extracting knowledge from high dimensional online content using automated techniques also increased. To address the decision making issues, a novel dynamic model is required to discover the required knowledge from these databases. In this paper, a novel filter based user navigation pattern mining model is designed and implemented on the large online steaming databases. Experimental results proved that the present filtered based frequent pattern mining model efficiently predicts the user navigation patterns with high accuracy and less runtime.