An Efficient Association Rule Mining Algorithm Based on Prejudging and Screening
ZHAO Xuejian①②③ SUN Zhixin①② YUAN Yuan③
① (School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ②(Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ③ (Jiangsu Posts & Telecommunications Planning and Designing Institute Co. LTD, Nanjing 210006, China)
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.
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