Abstract：In association rule mining, the importance of items is different and can not be subjectively given, which affects the mining result. The weighted items and weighted association rules are given, in which the weights of single attribute are determined by information entropy and the weights of items are determined by the compromise method between geometric mean and maximum weight value. Thus, the important projects are highlighted and the overall weights are balanced at the same time. On the basis of all above factors, weighted frequent patterns are extracted by using weighted frequent pattern tree, and the structure method of weighted frequent pattern tree is given. Finally, the experimental results on the spectral data of celestial body and the mechanical equipment EDEM verify the high efficiency of the proposed algorithm.
赵旭俊，蔡江辉，马洋. 基于信息熵的加权频繁模式树构造算法研究[J]. 模式识别与人工智能, 2014, 27(1): 28-34.
ZHAO Xu-Jun, CAI Jiang-Hui, MA Yang. Weighted Frequent Pattern Tree Structure Algorithm Based on Information Entropy. , 2014, 27(1): 28-34.
2014, Vol. 27 
