Abstract
Various fuzzy clustering algorithms have been proposed for vectorial data. However, these methods have not been applied to time-series data. This paper presents three fuzzy clustering algorithms for time-series data based on dynamic time warping (DTW). The first algorithm involves Kullback–Leibler divergence regularization of the DTW k-means objective function. The second algorithm replaces the membership of the DTW k-means objective function with its power. The third algorithm involves q-divergence regularization of the objective function of the first algorithm. Theoretical discussion shows that the third algorithm is a generalization of the first and second algorithms, which is substantiated through numerical experiments.
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Fujita, M., Kanzawa, Y. (2022). On Some Fuzzy Clustering Algorithms for Time-Series Data. In: Honda, K., Entani, T., Ubukata, S., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2022. Lecture Notes in Computer Science(), vol 13199. Springer, Cham. https://doi.org/10.1007/978-3-030-98018-4_14
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DOI: https://doi.org/10.1007/978-3-030-98018-4_14
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