Efficient Sanitization of Informative Association Rules with Updates

We propose here an efficient data-mining algorithm to sanitize informative association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicting item, an informative association rule set [16] is the smallest association rule set that makes the same prediction as the entire association rule set by confidence priority. Several approaches to sanitize informative association rules from static databases have been proposed [27]-[28]. However, frequent updates to the database may require repeated sanitizations of original database and added data sets. The efforts of previous sanitization are not utilized in these approaches. In this work, we propose using pattern inversion tree to store the added data set in one database scan. It is then sanitized and merged to the original sanitized database. Numerical experiments show that the proposed approach out performs the direct sanitization on original and added data sets, with similar side effects.