正负关联规则挖掘

Positive and Negative Association Rules

针对传统的关联规则数据挖掘的支持度-置信度框架存在很多缺陷，同时研究正负关联规则时可能产生很多问题的情况，阐述了在正负关联规则挖掘中，如何利用允许用户指定多重最小支持度来反应数据库中项的性质和它们各种各样的频率，并通过设置相关度提高挖掘效率．实验结果显示该方法是有效的．

The conventional framework for mining association is the support-confidence framework which has some limitation. When we study position and negative association rules simultaneously, many problems will occur, including the technique which allows the user to specify multiple minimum supports to reflect the natures of the iterns and their varied frequencies in the database. Then through setting correlation, strength efficiency of mining will be improved. At last. experiment results show that the technique is very effective.