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

  • Thomas Lampert
  • Thi-Bich-Hanh Dao
  • Baptiste Lafabregue
  • Nicolas Serrette
  • Germain Forestier
  • Bruno Crémilleux
  • Christel Vrain
  • Pierre Gançarski
Article

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 constrained 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 exhaustive review of constrained clustering algorithms and by modifying publicly available implementations to use a more appropriate distance measure—dynamic time warping. 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 computationally expensive and unstable under these modifications. Spectral approaches are easily applied and offer state-of-the-art performance, whereas declarative approaches are also easily applied and guarantee constraint satisfaction. An analysis of the results raises several influencing factors to an algorithm’s performance when constraints are introduced.

Keywords

Constrained clustering Semi-supervised clustering Partition clustering Time-series Dynamic time warping 

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Thomas Lampert
    • 1
  • Thi-Bich-Hanh Dao
    • 2
  • Baptiste Lafabregue
    • 1
  • Nicolas Serrette
    • 2
  • Germain Forestier
    • 3
  • Bruno Crémilleux
    • 4
  • Christel Vrain
    • 2
  • Pierre Gançarski
    • 1
  1. 1.ICubeUniversity of StrasbourgStrasbourgFrance
  2. 2.LIFOUniversity of OrléansOrléansFrance
  3. 3.MIPSUniversity of Haute-AlsaceMulhouseFrance
  4. 4.GREYCUniversity of Caen NormandieCaenFrance

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