Based on the introduction of data miningtechnology and Apriori algorithm, the paper provides a detailed review of theapplication of Association Rule according to the empirical analysis. Throughdemonstrations, it is proved that putting Association Rule Mining technologyinto the 3PL(short for Third-party Logistics Enterprise) information managementis not only feasible in terms of technology, but also helpful for theseenterprises to conduct a marketing analysis and make a scientific decision intime according to the intrinsic association regulations founded through mining.

1.   Introduction

With the logistics industry’s rapid developmentand continuous rise of logistics information level, the volume of logisticsdata grows on a geometric level. It is difficult to analyze these tremendousdata deeply by a traditional way. However, the data mining technologies such asAssociation Rule are better processing tools to solve this kind of problems.According to the application of Association Rule mining technology in thelogistics field, analyzing the vast logistics information and excavating their potential values are ofadvantage for the management layer of 3PL to find the intrinsic associationregulation in time and provide scientific guidance for decision-makings such asmarketing^[1] .

2.   Logistics Information Mining

Data Miningis also called Knowledge Discovery in Database(KDD). Berry and Linoff^[2]describe that Data Mining is a technology which uses automatic orsemi-automatic analysis to find out the meaningful relationships and the lawsof a large amount of data. However, Grupe and Owrang^[3]argue that Data Mining is to achieve new facts from the existeddata and discover the new relationships that experts still don’t know atpresent. To sum up, Data Mining is a process that we can extract the potentialand valuable knowledge (models or rules) from^[4]. Association Rule is based on the system structure of support and confidence^[5] [6] ,it isconsidered as one of the common data mining technologies, which can effectivelyfind the links among data and predict the market trends from the existed data.Therefore, it has a wide range of uses in customer relationship management and marketingstrategy- making ^[7][8].