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https://doi.org/10.1109/fuzz.2...
Article . 2003 . Peer-reviewed
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Conference object . 2020
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Fuzzy information granules in time series data

Authors: Ortolani, Marco; Hofer, Heiko; Patterson, David; Höppner, Frank; Berthold, Michael R.;

Fuzzy information granules in time series data

Abstract

It is often desirable to summarize a set of time series through typical shapes in order to analyze them. The algorithm presented here compares pieces of different time series in order to find similar shapes. The use of a fuzzy clustering technique based on fuzzy c-means allows us to consider such subsets belonging to typical shapes with a degree of membership. Additionally, this matching is invariant with respect to a scaling of the time series. The algorithm is demonstrated on a widely known set of data taken from the ECG rhythm analysis experiments performed at MIT laboratories.

Country
Germany
Keywords

info:eu-repo/classification/ddc/004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
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