负增量关联规则更新算法研究

Study of Updating Algorithm for Negative Incremental Association Rule

在增量式关联规则更新算法的研究中,关于负增量式更新算法的研究比较少。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。算法在如何减少数据集的扫描次数,如何充分利用现有的信息减少候选集的规模等方面进行了研究,给出了算法的具体实现。通过分析,算法是可行的。

In the study of updating algorithm for incremental association rules,litde research has been done on the negative incremental updating algorithm. 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. The algorithm explores how to diminish the number of scanning data sets, and how to make the best use of known information to shorten the size of candidate sets and so on. The concretization of the algorithm is also given. To sum up, the algorithm is feasible through analysis.