Mining interest in online shoppers’ data: An association rule mining approach

Abstract

Online shopping, as a form of e-commerce, is not nearing extinction anytime soon. As the interplay between shoppers and vendors continues to grow in the midst of complex transactional data, extracting knowledge from the data has become imperative. In view of this, this paper explores the use of the association rule mining technique to glean relevant information from such shopper-vendor interactions. In particular, this paper looks at some of the unusual, frequent relationships existing between online shoppers on one hand, and vendors on the other hand in the Czech Republic. The results revealed with higher confidence values the following: (1) there is a strong association between criteria for buying items on the Internet and information gathered before initiating an online transaction; (2) a sizable number of online customers engage in online shopping because of the price attached to the product in question; and (3) a greater proportion of online customers engage in online transactions through specialized e-shops. The work provides general insights into how shopper-vendor transactional data can be explored.