A Matrix Mining Method with FP-Tree for Generation of Frequent Patterns

Zhenyu Liu, Qingqing Sun, Jing Li

Abstract


In order to mining frequent patterns more efficiently, in this paper we propose a matrix mining method based on the FP-tree to generate the frequent patterns. This method overcomes the recursive generation of conditional pattern trees which cost the vast majority time and memories of the FP-tree grow method. It mines the frequent itemsets with a local frequent items matrix based on the item in question which needs twice scans of the FP-tree to construct the matrix, then the frequent patterns mining will be done through the operation on the matrix. We provide an implementation of the method with a simple example and proved the efficiency of the proposed method comparing with conditional pattern tree by discussing the structure and principle of the method.

Keywords


Frequent patterns, local frequent items, item matrix

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