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Abstract: Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDB includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series... (Update)
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BibTeX entry: (Update)
M. Last, Y. Klein, A. Kandel, "Knowledge Discovery in Time Series Databases," IEEE Transactions on Systems, Man, and Cybernetics 31B(2001). http://citeseer.ist.psu.edu/last01knowledge.html More
@misc{ last01knowledge,
author = "M. Last and Y. Klein and A. Kandel",
title = "Knowledge Discovery in Time Series Databases",
text = "M. Last, Y. Klein, A. Kandel, Knowledge Discovery in Time Series Databases,
IEEE Transactions on Systems, Man, and Cybernetics 31B(2001).",
year = "2001",
url = "citeseer.ist.psu.edu/last01knowledge.html" }
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