Market Basket Analysis/Association Rule Mining using R package – arules

In my previous post, i had discussed about Association rule mining in some detail.  Here i have shown the implementation of the concept using open source tool R using the package arules. Market Basket Analysis is a specific application of Association rule mining, where retail transaction baskets are analysed to find the products which are likely to be purchased together. The analysis output forms the input for  recomendation engines/marketing strategies.

Association Rule Mining

Association Rule Mining [ Implementation using R here]

Association Rule mining is one of the classical DM technique. Association Rule mining is a very powerful technique of analysing / finding patterns in the data set. It is a supervised learning technique in the sense that we feed the Association Algorithm with a training data set( as called Experience E in machine learning context) to formulate hypothesis(H) . The input data to a association rule mining algorithm requires a format which will be detailed shortly.
Ok let me first introduce the readers with some of the application areas of this DM technique and motivation for the study of Association analysis. The classic application of the association rule mining is to analyse the Market Basket Data of a retail store. For example, Retail stores like Wal-Mart, Reliance fresh, big bazaar gather data about customer purchase behaviour and they have complete details of the goods purchased as part of a single bill. This is called Market basket data and its analysis is termed “market basket analysis”.