Weighted Based Frequent and Infrequent Pattern Mining Model for Real-time E-Commerce Databases

In modern system e-commerce is developing in fast and it makes the availability of resources and services on the internet colorful. In today's e-commerce world day by day the data is increasing tremendously and this data should be used effectively. Data mining techniques produce useful knowledge for decision makers from high dimensional databases. Association rule mining is a used in e-commerce data analysis to realize cross selling and patterns generated can be used as recommendation system. Numerous models have been studied in both frequent as well as infrequent pattern mining in marketing applications which have some unsolved issues yet. A novel weighted based frequent and infrequent pattern mining model for real time e-commerce databases is proposed to find weighted based frequent and infrequent patterns from large data bases. Here weighted infrequent ranking measure is used to filter the infrequent product from the frequent associations. In this model a real-time e-commerce application is designed for pattern extraction process. This model is implemented in Java on real time e-commerce database (flip cart database). This model generates weighted based frequent and infrequent patterns based on user selected feature product in e-commerce database. This model is also implemented on distributed market database (training database), cloud database and medical database.