An Inference Mechanism Framework for Association Rule Mining

Abstract

Available approaches for Association Rule Mining (ARM) generates a large number of association rules, these rules may be trivial and redundant and also such rules are difficult to manage and understand for the users. If we consider their complexity, then it consumes lots of time and memory. Sometimes decision making is impossible for such kinds of association rules. An inference approach is required to resolve this kind of problem and to produce an interesting knowledge for the user. In this paper, we present an inference mechanism framework for ARM, which would be capable enough for resolving such problems, it would also predict future possibilities using Markov predictor by analyzing available fact and inference rules.