An efficient algorithm for the incremental updation of association rules in large databases

This paper proposes 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.

Abstract

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. Introduction Database mining, or knowledge discovery in databases (KDD) has recently attracted tremendous amount of attention in the database research community because of its wide applicability in many areas, including decision support, market strategy and financial forecast. One of the i...