DocumentCode :
1317215
Title :
SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis
Author :
Fisch, Dominik ; Gruber, Thiemo ; Sick, Bernhard
Author_Institution :
Dept. of Inf. & Math., Univ. of Passau, Passau, Germany
Volume :
23
Issue :
5
fYear :
2011
fDate :
5/1/2011 12:00:00 AM
Firstpage :
774
Lastpage :
787
Abstract :
In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.
Keywords :
data mining; pattern classification; polynomials; time series; ECG data; Lorenz attractor time series; SwiftRule; classification rule; dynamic classifier; energy consumption; generative model; human domain expert; polynomial model; polynomial modeling; short term trend; temporal data mining; SwiftRule.; Temporal data mining; anomaly detection; generative classifier; piecewise polynomial representation; piecewise probabilistic representation; time series classification;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2010.161
Filename :
5567100
Link To Document :
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