An efficient mining method for incremental updation in large databases

Abstract

The database used in mining 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 incoming dataset, we adopt the idea of negative border to help reduce the number of scans over the original database and discover new itemsets in the updated database. 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 our method runs faster than other incremental mining techniques, especially when the large itemsets in the updated database are significantly different from those in the original database. © Springer-Verlag 2003.