@inproceedings{2c7746225e564ae6831b41a177663f22,
title = "On Some Fuzzy Clustering Algorithms for Time-Series Data",
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.",
keywords = "Dynamic time warping, Fuzzy clustering, Time-series data",
author = "Mizuki Fujita and Yuchi Kanzawa",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2022 ; Conference date: 18-03-2022 Through 19-03-2022",
year = "2022",
doi = "10.1007/978-3-030-98018-4_14",
language = "English",
isbn = "9783030980177",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "169--181",
editor = "Katsuhiro Honda and Tomoe Entani and Seiki Ubukata and Van-Nam Huynh and Masahiro Inuiguchi",
booktitle = "Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Proceedings",
}