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Constrained distance based clustering for time-series: a comparative and experimental study - 2018

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

Research Area:  Big Data

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 algorithms performance when constraints are introduced.

Keywords:  

Author(s) Name:  Thomas Lampert, Thi-Bich-Hanh Dao, Baptiste Lafabregue, Nicolas Serrette, Germain Forestier, Bruno Crémilleux, Christel Vrain and Pierre Gançarski

Journal name:  Data Mining and Knowledge Discovery

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s10618-018-0573-y

Volume Information:  volume 32, pages 1663–1707 (2018)