Incremental maintenance of discovered association rules and approximate dependencies

Association Rules (ARs) and Approximate Dependencies (ADs) are significant fields in data mining and the focus of many research efforts. This knowledge, extracted by traditional mining algorithms becomes inexact when new data operations are executed, a common problem in real-world applications. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful to extract knowledge in dynamic environments. However, the implementation of algorithms only to maintain previously discovered information creates inefficiencies. In this paper, two active algorithms are proposed for incremental maintenance of previous discovered ARs and ADs, inspired by efficient computation of changes. These algorithms operate over a generic form of measures to efficiently maintain a wide range of rule metrics simultaneously. We also propose to compute data operations at real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain previously extracted valuable information. Experimental results in real education data and repository datasets show that our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.