基于滑动窗口的关联规则增量式更新算法

现有的关联规则算法大多都致力于解决增量式更新问题，需要多次扫描数据集，无法对海量数据进行有效处理。针对此问题，提出了基于滑动窗口的关联规则增量式更新算法(SWIUA)，利用滑动窗口进行数据更新，挖掘出用户感兴趣的关联规则。该算法只需要扫描原始数据集和更新的数据各一遍，降低了I/O时间；并采用优化策略对候选项集过滤和删除，提高了关联规则的挖掘性能，能有效处理大量新增数据。

Incremental updating algorithm for association rule based on sliding-window

Most of the present association rule algorithms are devoted to the problem of incremental updating, and need to scan the database several times. It is difficult to deal with the large data effectively. The incremental updating algorithm for association rule based on sliding-window (SWIUA) was proposed. This algorithm updates the data by sliding-window, deals with the large new data effectively, and gets the interested and new association rules. It scans the original database and updates data only once, reduces the time of I/O and improves the mining performance of association rules.