Discovery of Quantitative Sequential Patterns from Event Sequences
Discovery of Quantitative Sequential Patterns from Event Sequences
复制标题
DOI：
10.1109/icdmw.2009.13
复制
发表时间：
2009-12
期刊：
影响因子：
--
通讯作者：
Fumiya Nakagaito;Tomonobu Ozaki;T. Ohkawa
中科院分区：
文献类型：
--
作者：
Fumiya Nakagaito;Tomonobu Ozaki;T. Ohkawa
本文研究了具有时间间隔的时态事件数据库中的频繁模式挖掘问题。由于定量的时间信息可能在许多应用领域中发挥重要作用，因此发现与数值属性相关联的模式至关重要。为此，我们考虑两种时间模式的持续时间和时间差的定量信息的事件，并提出相应的算法，将数值聚类技术到现有的时间模式矿工。通过使用真实的世界数据集来评估所提出的算法的有效性。
In this paper, we consider the problem of frequent pattern mining in databases of temporal events with intervals. Since quantitative temporal information might play important roles in many application domains, it is critical to discover patterns to which numerical attributes are associated. To this end, we consider two kinds of temporal patterns with quantitative information on the durations and time differences of events, and propose corresponding algorithms by incorporating numerical clustering techniques into existing temporal pattern miners. The effectiveness of the proposed algorithms was assessed by using real world datasets.