FP-tree based association rule mining using MXML

Madderi Sivalingam Saravanan, R J Rama Sree

Abstract


The FP-tree (Frequent Pattern tree) based association rule mining is currently one of the fastest approaches to frequent item set mining. In this paper we describe the FP-tree based association rule mining using MXML (Mining eXtensible Markup Language) script, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection. Hence, finally we report these experimental results by comparing this implementation of the FP-growth algorithm with Apriori algorithm.


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