Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations

Abstract

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.

DOI: 10.1137/1.9781611972757.63

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@inproceedings{Id2005KnowledgeDF, title={Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations}, author={Tsuyoshi Id{\'e} and Keisuke Inoue}, booktitle={SDM}, year={2005} }