Mining Temporal Features in Association Rules
Mining Temporal Features in Association Rules
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DOI：
10.1007/978-3-540-48247-5_33
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发表时间：
1999-09
期刊：
影响因子：
--
通讯作者：
Xiaodong Chen;I. Petrounias
中科院分区：
文献类型：
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作者：
Xiaodong Chen;I. Petrounias
在现实世界的应用程序中，用于辅助决策的知识总是时变的。然而，大多数现有的数据挖掘方法依赖于发现的知识无限期有效的假设。期望使用发现的知识的人可能不知道它何时变得有效，或者它在现在是否仍然有效，或者它是否会在将来的某个时候有效。为了支持更好的决策制定，最好能够实际识别具有有趣模式或规则的时态特征。本文主要关注的是模式和更具体的关联规则的有效周期和周期性的识别。
In real world applications, the knowledge that is used for aiding decision-making is always time-varying. However, most of the existing data mining approaches rely on the assumption that discovered knowledge is valid indefinitely. People who expect to use the discovered knowledge may not know when it became valid, or whether it still is valid in the present, or if it will be valid sometime in the future. For supporting better decision making, it is desirable to be able to actually identify the temporal features with the interesting patterns or rules. The major concerns in this paper are the identification of the valid period and periodicity of patterns and more specifically association rules.