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 is considered as one of the common datamining technologies, which can effectively find the links among data andpredict the market trends from the existed data. Therefore, it has a wide rangeof uses in customer relationship management and marketing strategy- making [7][8].
With the development of logistics informationlevel, it is of great significance for the logistics enterprises and users toanalyze the direction of goods flow. With the help of Association Rule Mining,logistics enterprise can forecast the products that the customers may beinterested in without increasing cost and purposely popularize product groups. Throughanalyzing the customs’ goods delivery data with the application of AssociationRule Mining, logistics enterprise can find the association among deliverydirections as well as the association between delivery directions and deliveryspecies[9] [10]. Meanwhile, according to the intrinsic association law of data mining,logistics enterprise can make the marketing analysis report, list the potentialtarget customers, purposely expand the business, sell the service that customerneeds and improve the business marketing success rate, which will create moreeconomic benefit.
3. Empirical Analysis
As a third-party logistics enterprise,Yuancheng Group has a sound logistics network in all provincial capital citiesas well as the second and third economic developed cities in