Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations


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

Extracted Key Phrases

5 Figures and Tables


Citations per Year

73 Citations

Semantic Scholar estimates that this publication has 73 citations based on the available data.

See our FAQ for additional information.

Cite this paper

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