Efficient generation of Adaptive-Support Association Rules

We propose here an efficient data-mining algorithm to discover Adaptive-Support Association Rules (ASAR) from databases. Adaptive-support association rules are constrained association rules with application to collaborative recommendation systems. To discover association rules for recommendation systems, a specific value of target item in association rules is usually assumed and no minimum support is specified in advance. Based on size monotonocity of association rules, i.e., the number of association rules decreases when the minimum support increases, an efficient algorithm using adjustable step size for finding minimum support and therefore adaptive-support association rules is presented. Experimental comparison with the fixed step size adjustment approach shows that our proposed technique requires less computation, both running time and iteration steps, and will always find a corresponding minimum support.