An Enhanced Frequent Pattern-Growth Algorithm with Dual Pruning using Modified Anti-Monotone Support

摘要: Pattern discovery does not only end when a process obtained certain pattern. It also requires careful evaluation to show whether the pattern is significant enough support any decision-making. Generating interesting frequent important remove uninteresting and weak rules. The study, Dual Pruned Frequent Pattern-Growth (2P FP-Growth), enhanced FP-Growth algorithm by performing dual pruning of itemsets before generating patterns. 2P first removed satisfying minimum count, which represent pruning. Consequently, constructed FP tree. Secondly, traverses each subtree, removing nodes in subtree that do satisfy constituting second used modified anti-monotone constraint removes meet with but entire subtree. confidence measure resulting patterns showed most 1.0, while obtaining interdependency result less than 1.0. comparative implies association rule’s performance negatively interdependent its predicted response.