Borders: An efficient algorithm for association generation in dynamic databases
We consider the problem of finding association rules in a database with binary 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 deleted. At any given time, the rules for the current set of data are of interest. The naive, and highly inefficient, solution would be to rerun the association generation algorithm from scratch following the arrival of each new batch of data. This paper describes the Borders algorithm, which provides an efficient method for generating associations incrementally, from dynamically changing databases. Experimental results show an improved performance of the new algorithm when compared with previous solutions to the problem.
association rules data mining knowledge discovery