摘要： 随着各种形式的数据的迅速增长，业务数据中的时态信息挖掘问题受到人们普遍关注。该文提出了一种带有效时间区间的时态关联规则，给出了一种基于FP-树的挖掘方法。该方法利用分区挖掘的思想，以分区为单位表示项集的有效时间区间，并为每个分区构建FP-树，大大简化了对某个项集在其有效时间区间中的出现次数的计算，从而更有效地计算时态置信度。最后用一个例子对该方法的执行过程进行了阐述。

关键词: 数据挖掘, 时态数据挖掘, 关联规则, 频繁模式树

Abstract: With the rapid growth of data available from all kinds of sources, temporal information mining in business data has been a hot area attracting more and more attention. An approach to discover temporal association rules within valid time intervals is investigated. An algorithm based on FP-tree is devised. In this approach, by means of partition mining, each itemset is associated with a valid time interval presented by units of partitions. For each partition, a FP-tree is constructed to help the calculation of the count of an itemset within its valid time interval, which can smooth the calculation of temporal confidence. At last, an example is given to demonstrate the mining process.

Key words: Data mining, Temporal data mining, Association rule, Frequent pattern tree

中图分类号: 

• TP311