数据挖掘的任务有关联分析、时序模式、聚类、分类与预测等。
The tasks of data mining include association rules analysis, time series module, cluster analysis, classification and predication and so on.
序列模式挖掘作为一种时序数据分析的有效手段，能够自动从告警中提取出有助于关联分析的情景规则。
As an effective means to analyze timed data sequential pattern mining can extract episode rules from alarms, which is helpful to analyze correlation.
其思想是通过将网络审计数据转化为时序数据库，对其进行序列模式挖掘以提炼出用户行为模式，并由此进行异常检测。
The idea is to transform the net audit data into time series database and mine the sequence pattern to extract the user behavior pattern , and then to use behavior pattern in anomaly detection.
数据挖掘的任务主要有关联分析、聚类分析、分类、预测、时序模式和偏差分析等。
The main task of data mining includes correlation analysis, cluster analysis, classification, prediction, time-series pattern, deviation analysis and so on.
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.
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.
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