基于预判筛选的高效关联规则挖掘算法

doi:10.11999/JEIT151107

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Abstract

Association rule analysis, as one of the significant means of data mining, plays an important role in discovering the implicit knowledge in massive transaction data. To overcome the inherent defects of the classic Apriori algorithm, this paper proposes Apriori With Prejudging (AWP) algorithm. AWP algorithm adds a pre-judging procedure on the basis of the self-join and pruning progress in Apriori algorithm. It reduces and optimizes the k-frequent item sets using prior probability. In addition, the damping factor and compensating factor are introduced to revise the deviation caused by pre-judging. AWP algorithm simplifies the operation process of mining frequent item sets. Experimental results show that the improvement measures can effectively reduce the number of scanning databases and reduce the running time of the algorithm.

Key words： Data mining Association rules Transaction database Prejudging Apriori

Received: 29 September 2015 Published: 14 April 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61373135, 61401225, 61502252, 61201160), Natural Science Foundation of Jiangsu Province of China (BK20140883, BK20140894, BK20131377), China Postdoctoral Science Foundation Funded Project (2015M581844), Jiangsu Planned Projects for Postdoctoral Research Funds (1501125B), NUPTSF (NY214101, NY215147)

Corresponding Authors: ZHAO Xuejian E-mail: zhaoxj@njupt.edu.cn

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	ZHAO Xuejian
	SUN Zhixin
	YUAN Yuan

Cite this article:

ZHAO Xuejian,SUN Zhixin,YUAN Yuan. An Efficient Association Rule Mining Algorithm Based on Prejudging and Screening[J]. JEIT, 2016, 38(7): 1654-1659.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151107 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I7/1654

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