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.
在此基础上提出一种基于改进差别矩阵的核增量式更新算法，主要考虑对象动态增加情况下核的更新问题。
The authors introduced an incremental updating algorithm for computing core based on improved discernibility matrix, which mainly considered core updating when objects dynamically increased.
在此基础上提出一种基于改进差别矩阵的核增量式更新算法，主要考虑对象动态增加情况下核的更新问题。
The authors introduced an incremental updating algorithm for computing core based on improved discernibility matrix, which mainly considered core updating when objects dynamically increased.
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