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Keywords
(8)
Conditional Probability
Frequent Pattern
Maximum Likelihood Estimate
Sequential Pattern
Stochastic Model
Time Series
Transition Probability
Markov Model
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Rule generation for categorical time series with Markov assumptions
Rule generation for categorical time series with Markov assumptions,10.1007/s112220099141z,Statistics and Computing,Christian H. Weiß
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Rule generation for categorical time series with Markov assumptions
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Christian H. Weiß
Several procedures of
sequential pattern
analysis are designed to detect frequently occurring patterns in a single categorical
time series
(episode mining). Based on these frequent patterns, rules are generated and evaluated, for example, in terms of their confidence. The confidence value is commonly interpreted as an estimate of a conditional probability, so some kind of
stochastic model
has to be assumed. The model is identified as a variable length Markov model. With this assumption, the usual confidences are
maximum likelihood
estimates of the transition probabilities of the Markov model. We discuss possibilities of how to efficiently fit an appropriate model to the data. Based on this model, rules are formulated. It is demonstrated that this new approach generates noticeably less and more reliable rules.
Journal:
Statistics and Computing
, vol. 21, no. 1, pp. 116, 2011
DOI:
10.1007/s112220099141z
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References
(29)
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Journal:
Statistics and Computing
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Very Large Data Bases  VLDB
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(
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Rakesh Agrawal
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International Conference on Data Engineering  ICDE
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