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

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.
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Thomas Lampert, Thi-Bich-Hanh Dao, Baptiste Lafabregue, Nicolas Serrette, Germain Forestier, et al.. Constrained distance based clustering for time-series: a comparative and experimental study. Data Mining and Knowledge Discovery, Springer, 2018, 32 (6), pp.1663-1707. ⟨10.1007/s10618-018-0573-y⟩. ⟨hal-01831637⟩

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