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。
Temporal data mining can be defined as the search for interesting correlations or patterns in large sets of temporal data.
态数据挖掘可以定义为在大量时态数据集合中寻找有趣联系或模式的研究。
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。
Temporal data mining has the capability to discover patterns or rules which might be overlooked when the temporal component is ignored or treated as a simple numeric attribute.
当时态组成部分被忽视或作为简单数值属性而被忽视时，时态数据挖掘有发现被忽略的模式或规则的能力。
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。
Mining Temporal data is becoming increasingly essential in data mining.
时态数据挖掘是数据挖掘中一个日益重要的研究课题。
But due to the inherent characteristics of the multi data source, multi type, multi temporal and multi resolution, one data mining system to excavate all types of weather data is unrealistic.
但由于气象数据的多数据源、多类型、多时相、多分辨率等固有的特点，指望一个数据挖掘系统挖掘所有类型的气象数据是不现实的。
But due to the inherent characteristics of the multi data source, multi type, multi temporal and multi resolution, one data mining system to excavate all types of weather data is unrealistic.
但由于气象数据的多数据源、多类型、多时相、多分辨率等固有的特点，指望一个数据挖掘系统挖掘所有类型的气象数据是不现实的。
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