Constrained distance based clustering for time-series: a comparative and experimental study

Constrained distance based clustering for time-series: a comparative and experimental study Data Min Knowl Disc https://doi.org/10.1007/s10618-018-0573-y Constrained distance based clustering for time-series: a comparative and experimental study 1 2 1 Thomas Lampert · Thi-Bich-Hanh Dao · Baptiste Lafabregue · 2 3 4 Nicolas Serrette · Germain Forestier · Bruno Crémilleux · 2 1 Christel Vrain · Pierre Gançarski Received: 26 September 2017 / Accepted: 19 May 2018 © The Author(s) 2018 Abstract Constrained clustering is becoming an increasingly popular approach in data mining. It offers a balance between the complexity of producing a formal definition of thematic classes—required by supervised methods—and unsupervised approaches, which ignore expert knowledge and intuition. Nevertheless, the application of con- strained clustering to time-series analysis is relatively unknown. This is partly due to the unsuitability of the Euclidean distance metric, which is typically used in data mining, to time-series data. This article addresses this divide by presenting an exhaus- tive review of constrained clustering algorithms and by modifying publicly available implementations to use a more appropriate distance measure—dynamic time warp- ing. It presents a comparative study, in which their performance is evaluated when applied to time-series. It is found that k-means based algorithms become computa- tionally expensive and unstable under these modifications. Spectral approaches are easily http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Data Mining and Knowledge Discovery Springer Journals

Constrained distance based clustering for time-series: a comparative and experimental study

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Publisher
Springer US
Copyright
Copyright © 2018 by The Author(s)
Subject
Computer Science; Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
ISSN
1384-5810
eISSN
1573-756X
D.O.I.
10.1007/s10618-018-0573-y
Publisher site
See Article on Publisher Site

Abstract

Data Min Knowl Disc https://doi.org/10.1007/s10618-018-0573-y Constrained distance based clustering for time-series: a comparative and experimental study 1 2 1 Thomas Lampert · Thi-Bich-Hanh Dao · Baptiste Lafabregue · 2 3 4 Nicolas Serrette · Germain Forestier · Bruno Crémilleux · 2 1 Christel Vrain · Pierre Gançarski Received: 26 September 2017 / Accepted: 19 May 2018 © The Author(s) 2018 Abstract Constrained clustering is becoming an increasingly popular approach in data mining. It offers a balance between the complexity of producing a formal definition of thematic classes—required by supervised methods—and unsupervised approaches, which ignore expert knowledge and intuition. Nevertheless, the application of con- strained clustering to time-series analysis is relatively unknown. This is partly due to the unsuitability of the Euclidean distance metric, which is typically used in data mining, to time-series data. This article addresses this divide by presenting an exhaus- tive review of constrained clustering algorithms and by modifying publicly available implementations to use a more appropriate distance measure—dynamic time warp- ing. It presents a comparative study, in which their performance is evaluated when applied to time-series. It is found that k-means based algorithms become computa- tionally expensive and unstable under these modifications. Spectral approaches are easily

Journal

Data Mining and Knowledge DiscoverySpringer Journals

Published: May 30, 2018

References

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