数据流模式依赖挖掘及应用

Data Stream Pattern Dependency Mining and Its Application

针对数据流间"模式依赖"问题,给出了一种模式依赖挖掘算法,该算法包括:挖掘前时间序列分段和模式表示,条件规则元组的创建和维护,模式依赖的置信度和支持度计算,2个或N个数据流概要结构的设计等。股票数据实验和实际系统表明,该挖掘方法能够有效地发现数据流间的模式依赖,可用于预测。

This paper discusses problem of stock pattern dependency mining on data streams, proposes a mining algorithm. This algorithm includes pre-process steps such as time series segmentation and pattern presentation, using 4 item rule tuples to present the dependency and to calculate the confidence and support degree, and the synopsis structure for two and N data streams. Experiments on stock price data and a real system show that the algorithm is effective and can be used in forecasting.