This paper discusses some problems of temporal data mining that combine data mining with temporal data.
本文试图将数据挖掘和时态数据相结合来讨论时态数据挖掘的有关问题。
Spatio-temporal data mining is an important research topic in data mining, and in which spatiotemporal forecasting is the most widely used.
时空数据挖掘是数据挖掘中的重要研究内容，其中时空预测的应用领域最为广泛。
To overcome the defects of data representation algorithms in temporal data mining, segmentation algorithm of key-point-based error checking is proposed.
针对时序数据挖掘中常见数据表示算法的缺陷，提出了基于关键点的误差检验分段算法。
In this paper, the description of time expressions, the definition of temporal patterns and temporal association rules, and the description of the temporal data mining language (TDML) are presented.
首先简要讨论时间表达式的描述；然后提出有关时态模式和时态关联规则的若干定义；最后讨论时态数据挖掘语言(TDML)的描述并给出一个实例。
In this paper, the description of time expressions, the definition of temporal patterns and temporal association rules, and the description of the temporal data mining language (TDML) are presented.
首先简要讨论时间表达式的描述；然后提出有关时态模式和时态关联规则的若干定义；最后讨论时态数据挖掘语言(TDML)的描述并给出一个实例。
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