An adaptive algorithm for incremental mining of association rules

The association rules represent an important class of knowledge that can be discovered from data warehouses. Current research efforts are focused on inventing efficient ways of discovering these rules from large databases. As databases grow, the discovered rules need to be verified and new rules need to be added to the knowledge base. Since mining afresh every time the database grows is inefficient, algorithms for incremental mining are being investigated. Their primary aim is to avoid or minimize scans of the older database by using the intermediate data constructed during the earlier mining. We present one such algorithm. We make use of large and candidate itemsets and their counts in the older database, and scan the increment to find which rules continue to prevail and which ones fail in the merged database. We are also able to find new rules for the incremental and updated database. The algorithm is adaptive in nature, as it infers the nature of the increment and avoids altogether if possible, multiple scans of the incremental database. Another salient feature is that it does not need multiple scans of the older database. We also indicate some results on its performance against synthetic data