An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases

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Efficient discovery of association rules in large databases is a well studied problem and several approaches have been proposed. However, it is non trivial to maintain the association rules current when the database is updated since, such updates could invalidate existing rules or introduce new rules. In this paper, we propose an incremental updating technique based on negative borders, for the maintenance of association rules when new transaction data is added to or deleted from a transaction database. An important feature of our algorithm is that it requires a full scan (exactly one) of the whole database only if the database update causes the negative border of the set of large itemsets to expand.