对大型数据库中关联规则挖掘的频繁模式维护问题进行了研究,提出一种增量更新算法(Update Frequent Pattern List,UFPL)。该算法基于频繁模式表(FPL) .
基于4个网页-相关网页
提出了一种新的量化关联规则挖掘算法QAR及其增量式更新算法IUQAR。
A novel algorithm, QAR, for mining quantitative association rules and an incremental updating algorithm, IUQAR, are proposed.
另外根据实际应用背景，提出了一种混合数据立方体模型，并给出了其相应的查询和增量更新算法。
According to the practical context, this thesis raises a kind of hybrid data cube model and its query and incremental update algorithm.
提出了一种实用的在支持度和置信度不变的情况下数据集规模减小的负增量关联规则更新算法。
Provides a practical updating algorithm for negative incremental association rules in which the size of data sets is reduced, with the supporting and confidence limits unchanged.
应用推荐