Optimum session interval based on particle swarm optimization for generating personalized ontology

Abstract

A semantic web usage mining method is suggested to identify the association between consumer emotions and buying behaviors by utilizing the web log data. Fuzzy logic is used to signify the temporal conception and resource attributes for the requested URLS of web access activities. From this, a Personal Web usage Ontology is created which facilitates semantic web applications. But the limitation is less efficient in terms of accuracy and user satisfaction. Thus an innovative technique which is called Optimum Session Interval based Particle Swarm Optimization (OSIPSO) is introduced. This technique is used to find the optimum session interval. The Particle swarm optimization has no overlapping and mutation computation and it is proficient in global search. Additionally, an associative classification is used to enhance the accuracy. Associative classification is a combination of associative rule mining and classification rule mining. An experimental result shows that the proposed work has a high accuracy and high efficient.