Building on the arules infrastructure for analyzing transaction data with R

The free and extensible statistical computing environment R with its
enormous number of extension packages already provides many state-of-the-art techniques
for data analysis. Support for association rule mining, a popular exploratory
method which can be used, among other purposes, for uncovering cross-selling opportunities
in market baskets, has become available recently with the R extension
package arules. After a brief introduction to transaction data and association rules,
we present the formal framework implemented in arules and demonstrate how clustering
and association rule mining can be applied together using a market basket
data set from a typical retailer. This paper shows that implementing a basic infrastructure
with formal classes in R provides an extensible basis which can very
e±ciently be employed for developing new applications (such as clustering transactions)
in addition to association rule mining.