Mining Segment-Wise Periodic Patterns in Time-Related Databases
Mining Segment-Wise Periodic Patterns in Time-Related Databases
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发表时间：
1998-08
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通讯作者：
Jiawei Han;W. Gong;Yiwen Yin
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作者：
Jiawei Han;W. Gong;Yiwen Yin
周期性搜索，即在时间相关的数据库中搜索周期性，是一个有趣的数据挖掘问题。以前的大多数研究都是在寻找所选数据序列中所有段的全周期周期性，也就是说，如果序列是周期性的，则该周期中的所有点或段都会重复。然而，在时间相关的数据集中挖掘分段或逐点周期性通常很有用。在本研究中，我们集成了数据立方体和 Apriori 数据挖掘技术，用于挖掘固定长度周期的分段周期性，并表明数据立方体为多级周期性的交互式挖掘提供了有效的结构和便捷的方法。
Periodicity search, that is, search for cyclicity in time-related databases, is an interesting data mining problem. Most previous studies have been on finding full-cycle periodicity for all the segments in the selected sequences of the data, that is, if a sequence is periodic, all the points or segments in the period repeat. However, it is often useful to mine segment-wise or point-wise periodicity in time-related data sets. In this study, we integrate data cube and Apriori data mining techniques for mining segment-wise periodicity in regard to a fixed length period and show that data cube provides an efficient structure and a convenient way for interactive mining of multiple-level periodicity.