时态数据挖掘是数据挖掘中一个日益重要的研究课题。
Mining Temporal data is becoming increasingly essential in data mining.
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。
This paper discusses some problems of temporal data mining that combine data mining with temporal data.
周期模式主要是研究时序数据库中的循环特性，是时态数据挖掘的一个重要的研究方向。
Periodicity mining is the mining of periodic patterns, that is, the search for recurring patterns in time-series database.
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。
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.
本文表明确定学习可以为时态数据挖掘的研究提供新的途径，并为基于数据的建模与控制等问题提供新的研究思路。
The deterministic learning theory will provide a new approach to data-based modeling, recognition, control of complex processes and systems.
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.
该文针对网管告警数据库中时间序列存在的连续性、不确定性和模糊性问题，提出了一种基于时态关联规则挖掘告警库的新方法。
For the problems of continuity, uncertainty and fuzziness in the time-series of the network management alarm database, this pa-per puts forward a new mining method based on time-series rules.
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。
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。
Temporal data mining can be defined as the search for interesting correlations or patterns in large sets of temporal data.
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