Frequent pattern network mining algorithm based on transaction-item association matrix

Authors:
Yi-Jun Wang;Wei-Qing Sun;Jin-Tao She;Sheng-Biao Wei;Cheng-Min Wang
Affiliations:
Department of Electrical Engineering, Northeast Dianli University, Jilin, China;Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China;Nanping Electric Power Bureau, Nanping, Fujian, China;Nanping Electric Power Bureau, Nanping, Fujian, China;Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China
Venue:
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Year:
2009

Citing 5
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Data Mining: Concepts and Techniques

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Abstract

To increase the efficiency of data mining is the emphasis in this field at present. Aiming at the difficulties of data maintaining and updating in association rule mining FP-growth algorithm, this paper proposes a FP-network model which compresses the data needed in association rule mining in a FP-network. Compared with the primary FP-tree model, FP-network is undirected, which enlarge the scale of transaction storage; furthermore, the FP-network is stored through the definition of transaction-item association matrix, it is convenient to make association rule mining on the basic of defining node capability. Experiment results show that the FP-network mining association rule algorithm proposed by this letter not only inherits the merits of FP-growth algorithm, but also maintains and updates data conveniently. It improves the efficiency of association rule mining.