The Applied Research Of Association Rules Mining Technology In 3PL Enterprise Information System

Today,China is undergoing the process of comprehensive informatization,the informatization in logistics field is also in full swing, it is a general informatization, but also has its own characteristics.With the increasingly fierce competition of logistics market, the enterprises become more aware of the importance of information about decision-making, corporate executives are no longer merely use the experience or intuition to solve the issue of enterprise development. At the same time, with the rapid development of the logistics industry, logistics data also on the geometric progression growth, traditional information systems generally can not make deep analysis for the large amount of data ,as a result, managers are unable to understand thoroughly the laws of the development of the industry, and can not make accurate decisions. However, Data Mining technology will be able to better solve these problems.In this paper, the existing logistics information systems are analyzed. A 3PL enterprise information system is defined and its system structure and function model are studied. This paper proposes that the building of logistics information system should focus on appling and integrating of the existing technologies.The article introduces the application, CRISP-DM mining model, mining tools and related technologies of Data Mining and discusses in detail the concept, algorithms and improvement of Association Rules, and mainly studies the Apriori algorithm for exploring the Frequent Itemsets .A 3PL enterprise information system based on Association Rules Mining is proposed at the latter part of the article. Then the paper uses the Association Rule Mining to analyzes the historical data of a logistics enterprise and gets some inherent correlations between the products (referring to the logistics services) and products, customer and products.These results of the analysis provide some guiding recommendations for business decision-makers to make marketing strategies, shows that this research should be invested both in academic value and application value.