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Knowledge discovery from heterogeneous dynamic systems using change-point correlations (2005)
| Venue: | In ”Proceedings of the 2005 SIAM International Data Mining Conference |
| Citations: | 20 - 1 self |
BibTeX
@INPROCEEDINGS{Idé05knowledgediscovery,
author = {Tsuyoshi Idé and Keisuke Inoue},
title = {Knowledge discovery from heterogeneous dynamic systems using change-point correlations},
booktitle = {In ”Proceedings of the 2005 SIAM International Data Mining Conference},
year = {2005}
}
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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.
Keyphrases
knowledge discovery time series change-point correlation heterogeneous dynamic system singular spectrum transformation automobile data set direct comparison heterogeneous system probability density function useful dependency time-series data set correlated manner association rule causal relationship stream mining technique new nonlinear transformation various type particular change
