We consider the problem of finding association rules in a database with bin
ary attributes. Most algorithms for finding such rules assume that all the
data is available at the start of the data mining session. In practice, the
data in the database may change over time, with records being added and de
leted. At any given time, the rules for the current set of data are of inte
rest. The naive, and highly inefficient, solution would be to rerun the ass
ociation generation algorithm from scratch following the arrival of each ne
w batch of data. This paper describes the Borders algorithm, which provides
an efficient method for generating associations incrementally, from dynami
cally changing databases. Experimental results show an improved performance
of the new algorithm when compared with previous solutions to the problem.