An Associative Memory for Association Rule Mining

Abstract

Association rule mining is a thoroughly studied problem in data mining. Its solution has been aimed for by approaches based on different strategies involving, for instance, the use of novel data structures to represent the knowledge discovered, the transformation of the input data to speed up the process, the exploitation of the itemset properties either to traverse the possible itemset search space optimally or to form compact representation of the frequent itemsets employed for the generation of the corresponding final rules, and others. Surprisingly, biologically-inspired approaches have rarely been proposed. In this work, we focus on investigating if a type of mapping neural network, better known as an associative memory, is suitable for association rule mining. In particular, our aim is to determine if itemset support can be estimated from the knowledge embedded in the weight matrix of a trained associative memory in order to generate further association rules from such a knowledge.