Mining Association Rules for Construction Accessories Shop

Abstract

In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules describe events that tend to occur together. Association rules are “if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. This paper describes which products tend to be purchased together at the construction accessories shop. The proposed system has a large database of customer transcation. Each transcation consists of items purchased by a customer in a visit. In this paper, the system ppresents resultws of applying the apriori algorithm which is the standard algorithm to mine association rules for sales data of the construction.