A New Approach of Self-adaptive Discretization to Enhance the Apriori Quantitative Association Rule Mining

摘要：

Apriori algorithm was widely applied in association rule mining. Generally, we have to specify different ranges manually to discretize numeral fields to nominal fields, which may weaken the result due to unfit partitions. This paper introduced an approach to make discretized partitions in a self adaptive way to enhance the numeral quantitative association rule mining result.

关键词： Apriori algorithm Self-adapting discretization Association rule mining

作者: Li Dancheng Zhang Ming Zhou Shuangshuang Zheng Chen

作者单位: Northeastern University, Shenyang, Liaoning, 110004, China

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 44-47

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）