A general mining method for incremental updation in large databases

The database used for knowledge discovery is dynamic in nature. Data may be updated and new transactions may be added over time. As a result, the knowledge discovered from such databases is also dynamic. Incremental mining techniques have been developed to speed up the knowledge discovery process by avoiding re-learning of rules from the old data. To maintain the large itemsets against the updated database, we develop an approach named Negative Border using Sliding-Window Filtering (NB-SWF) which adopts the idea of the negative border and the sliding-window filtering algorithm. Negative border can help reduce the number of scans over the original database and the sliding-window filtering algorithm is to discover new itemsets in the updated database. By integrating the sliding-window filtering algorithm with the negative border, a lot of effort in the re-computation of negative border can be saved, and the minimal candidate set of large itemsets and negative border in the updated database can be obtained efficiently. Simulation results have shown that the NB-SWF runs faster than other incremental mining techniques, especially when there are few new large itemsets in the updated database.