Maintenance of discovered informative rule sets: incremental insertion

We propose here an efficient data-mining algorithm to discover the informative rule set (IRS) when the transaction database is updated, i.e., when a small transaction data set is added to the original database. An IRS is defined as the smallest subset of an association rule set such that it has the same prediction sequence by confidence priority as the association rule set. A top-down level-wise approach for the discovery of IRS on static database has been proposed. Based on the Fast UPdating technique (FUP) for the updating of discovered association rules, we present an algorithm to maintain the discovered IRS, under incremental insertion. Numerical comparison with the non-incremental informative rule set approach is shown to demonstrate that our proposed technique requires less computation time, in terms of number of database scanning, number of candidate rules generated and processing time, to maintain the discovered informative rule set.