Recommendatory Study Of Electronic Banking Products Based On Association Rules

In this paper, we discuss the electronic banking application based on association rules mining technology of data mining. Data mining is a process that extracts connotative, unknown, potentially valuable information and knowledge from the practical application of plentiful, incomplete, stochastic data. Association rules data mining is an important area of data mining, that is to find positive and meaningful rules from the data processing, therefore promote the development forwardly. Association rules technology is widely used in commercial, banking, telecommunications, insurance, medical and other industries.E-banking is the focus that commercial banks want to develop and market competition becomes increasingly fierce. As a mode of operation that mainly relies on human-computer interaction, how to improve its operational efficiency is a problem that commercial banks are researching deeply.This paper discusses the associated analysis and applied model that use association rule techniques. In the paper, we study deeply the historical transaction data for electronic banking and classification of the electronic banking products. We complete in-depth analysis for product recommendation of electronic banking products from different angles and different dimensions using MicroSoft Algorithm. Mainly work is as followings: (1) We find out systematic theory and category reasonably according as the analysis of E-banking products from the different characteristics. (2) Data processing and purification for multiple data sources. (3) We find out purchasing relation based on the analysis of log. We discover the connection between customer's own characteristics and electronic banking products based on the analysis of natural factors and social attributes of customer and purchasing behavior. We construct recommending model for different customers based on the associated data.