Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations
Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations
复制标题
使用变点相关性从异构动态系统中发现知识
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Keisuke Inoue
中科院分区：
文献类型：
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作者：
T. Idé;Keisuke Inoue
迄今为止，大多数流挖掘技术主要关注通过时间序列数据集之间的直接比较来发现关联规则。然而，他们的效用是非常有限的异构系统，其中各种类型的时间序列（离散，连续，振荡，噪声等）。以强相关的方式动态地起作用。在本文中，我们引入了一个新的非线性变换，奇异谱变换（SST），解决的问题，知识发现的因果关系的时间序列。SST是一种将时间序列转换为概率密度函数的转换，该函数表示观察到某些特定变化的机会。对于一个汽车数据集，我们证明了SST使我们能够发现变量之间隐藏的和有用的依赖关系。
Most of the stream mining techniques presented so far have primary paid attention to discovering association rules by direct comparison between time-series data sets. However, their utility is very limited for heterogeneous systems, where time series of various types (discrete, continuous, oscillatory, noisy, etc.) act dynamically in a strongly correlated manner. In this paper, we introduce a new nonlinear transformation, singular spectrum transformation (SST), to address the problem of knowledge discovery of causal relationships from a set of time series. SST is a transformation that transforms a time series into the probability density function that represents a chance to observe some particular change. For an automobile data set, we demonstrate that SST enables us to discover a hidden and useful dependency between variables.