Maintenance of Generalized Association Rules Based on Pre-large Concepts

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. In the past, researchers usually assumed databases were static and items were on a single level to simplify data mining problems. Thus, most of algorithms proposed focused on a single level, and did not utilize previously mined information in incrementally growing databases. Items in real world applications are, however, commonly with taxonomy. This paper thus proposes a maintenance algorithm for generalized association rules with taxonomy based on the concept of pre-large itemset. A pre-large itemset is not truly large, but promises to be large in the future. A lower and an upper support threshold are used to realize this concept. The two user-specified upper and lower support thresholds make the pre-large itemsets act as a gap to avoid small itemsets becoming large in the updated database when new transactions are inserted. The proposed algorithm doesn't need to rescan the original database until a number of transactions have been newly inserted. If the database has grown larger, then the number of new transactions allowed will be larger too. Key-Words: data mining, generalized association rule, taxonomy, large itemset, pre-large itemset.