Incremental Association Rule Mining Based on Artificial Immune
Most of the incremental association rule mining methods must rerun through processed data and have not made the best of the given rules. In this paper we propose an incremental association rules algorithm, this algorithm applies artificial immune theory and takes advantage of the given rules produced by original data set. Based on the quickly response during the memory cell recognizing the antigen, the algorithm is faster. The best rules are selected from the given rules as the optimum memory cell through promoting or inhibiting the antibodies, so the interest of the final rules is improved. The algorithm has better performance compared to FUP algorithm, especially in the number of the new transactions is less than half the number of transactions in the original data set, the timeconsuming of the FUP algorithm is more than 10 times to this algorithm.
association rules artificial immune incremental mining
Hanmei Liu Lianzhe Zhou Wei Xiao Limei Zhang
Computer Science and Engineering School C hangChun University of Technology ChangChun, JiLin, PR Chi Computer Science and Engineering School ChangChun University of Technology ChangChun, JiLin, PR Chin
国际会议
长春
英文
204-207
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）