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The authors have concluded that the bag of patterns representation (BoPR) approach performed better for finding similarities in the time series data as compared to other methods.
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Using the amplification modeling and pattern discovery approach, presented in the current study, fixing of finite amplicon structures became feasible and amplification patterns, representations of amplicon structures, were determined from the DNA copy number amplification models.
Other perspectives include testing data-driven containment strategies based on the various contact patterns representations and understanding how the evaluation of their performance depends on the representation, in particular for the case of the novel representation defined by the contact matrix of distributions we introduced here.
The "pattern" representation noted above is actually a form of symbolization.
Pattern representation is the first step toward clustering or classification.
Pattern representation indicates some kind of changing features, which can summarize and represent the time series.
This method should focus on two aspects: pattern representation and similarity matching.
The design of a recognition system requires careful attention to pattern representation and classifier design.
The PRDC-CSOR is an extension of the authors' previously proposed pattern representation scheme using data compression (PRDC).
Given a pre-set positive threshold η, LTP [20] can obtain a series of ternary values for local pattern representation.
Results show the value and potential of the new feature in machinery fault pattern representation and classification.
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