Consensus Clustering of Time Series Data

Date

2014-01-01

Author

Kursun, Ayca Yetere
İyigün, Cem
Batmaz, İnci

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In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. However, DTW is a similarity measure that is employed for single variable with standard clustering methods rather than consensus clustering. Thus our motivation is to create an algorithm that can combine the benefits of the DTW with benefits of consensus clustering, which will also provide a solution for multivariate applications. We present the results of our study both with simulated data and well known datasets from the literature.

Subject Keywords

Consensus Clustering, Dynamic Time Warping, Ensemble Clustering, Time Series Clustering

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85183579367&origin=inward
https://hdl.handle.net/11511/117787

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Department of Industrial Engineering, Conference / Seminar