A global averaging method for dynamic time warping, with applications to clustering

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

A global averaging method for dynamic time warping, with applications to clustering

Presenter : Jiang-Shan Wang

Authors : Francois Petitjean, Alain Ketterlin, Pierre Gancarski

PR 2011

國立雲林科技大學National Yunlin University of Science and Technology

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Outline

Motivation

Objective

Method

Experiment

Conclusion

Comments

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Motivation

To improve the drawbacks of previous studies. Pairwise averaging => sensitive to the order.

Local averaging => initial approximation error propagate.

To avoid long and detailed average sequences. Because the complexity of Dynamic Time Warping (DTW) is directly

related to the length of the sequences.

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Objective

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To propose a global averaging method for dynamic time warping.

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Method

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DTW barycenter averaging(DBA)

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiment

Datasets

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiment

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiment

Different initialization

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiment

Adaptive scaling(AS)

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiment

Satellite image time series

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Conclusion

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DBA achieves better results on all tested datasets and its behavior is robust.

Adaptive scaling is shown to shorten the average sequence in adequacy to DTW and to the data, but also to improve its representativity.

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Comments

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Advantage Many experiments

Reducing computational complexity

Drawback Some mistake

Application Sequence data clustering