Weighted Based Frequent and Infrequent Pattern Mining Model for Real-time E-Commerce Databases Sujatha Kamepalli 1* , Srinivasa Rao Bandaru 2 1 Department of Information Technology, VFSTR Deemed to be University, Vadlamudi Village, Guntur District, Andhra Pradesh 522213, India 2 Department of Management Studies, VFSTR Deemed to be University, Vadlamudi Village, Guntur District, Andhra Pradesh 522213, India Corresponding Author Email: sujatha101012@gmail.com https://doi.org/10.18280/ama_b.622-404 ABSTRACT Received: 10 October 2018 Accepted: 24 March 2019 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. Keywords: data mining, frequent pattern mining, infrequent pattern mining, e-commerce 1. INTRODUCTION Data, the collection of raw facts, is considered as basic form of information. This data has to undergo a number of steps in order to create knowledge. The steps are collection, management, mining and interpretation [1]. In today's e- commerce world day by day the data is increasing tremendously and this data should be used effectively. Collected data must be systematically processed and arranged properly by various techniques and tools as databases and data warehouses. Data mining tools were applied on that data for discovering hidden knowledge. As the size of data increases, it creates many problems in detecting the hidden patterns among various attributes. The discovered patterns are so important in e-commerce system in order to make decisions and e-commerce system provides a best work bench for data mining [2-4]. Cross selling analysis is a marketing method which is helpful in improving customer value, for maintaining more relations between the enterprise and the customer, and it demonstrates that the more dependent the customer will be on the enterprise, the higher the loyalty will be [5]. Association pattern mining is one of the data mining processes used in e-commerce. It examines the buying habits of various customers and helps in deciding the layout of the products in the virtual store and how promotions are to be bundled. It can also be used to improve the sales of different products by suggesting additional products that are related to the purchased products for the customer. In addition, in e- commerce, latest products have to be launched regularly while existing ones are excited to assure the increasing demanding needs of customers. From the discussion it is clear that the items in database are changed often. Unfortunately, existing algorithms assume that the set of unique items is fixed and hence, each time items are added or removed, the algorithms must discard valuable past mined results and re-mine the database. Finally, because the data in e-commerce is far too dynamic and volatile, there is no good way to decide on a suitable support threshold for the mining process. Using too high a threshold may result in too many useless rules while too low a threshold may result in certain important rules being passed over. Therefore, the database must be mined with several different support thresholds before an optimal threshold can be determined [6]. 2. LITERATURE REVIEW 2.1 FP-Growth* algorithm FP-Growth* algorithm basically implemented the data structure of traditional FP-Tree technique and merged it with array-based approaches. This will produce numbers of optimization approaches. Array based approaches are responsible to decrease the overall traversal time of the Advances in Modelling and Analysis B Vol. 62, No. 2-4, December, 2019, pp. 53-60 Journal homepage: http://iieta.org/journals/ama_b 53