Clustering time series under trend-oriented fuzzy information granulation

Yang, Xiyang; Yu, Feihong; Pedrycz, Witold; Li, Zhiwei

doi:10.1016/j.asoc.2023.110284

Cited by 9 publications

(1 citation statement)

References 30 publications

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“…Recently, continued attempts have been made to improve the performance of each method. As examples, studies published in the last three years are introduced as follows: the extension of dynamic time warping (DTW) 21,22 , the measure based on quantile cross-spectral density 23 , and the measure of two linear fuzzy information granule time-series 24 have been proposed to calculate the similarity or distance. A clustering method that focuses on the timevarying moment was proposed for financial time-series data 25 .…”

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confidence: 99%

Clustering method for time-series images using quantum-inspired digital annealer technology

Inoue,

Kubota,

Ikami

et al. 2024

Commun Eng

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Time-series clustering is a powerful data mining technique for time-series data in the absence of prior knowledge of the clusters. Here we propose a time-series clustering method that leverages an annealing machine, which accurately solves combinatorial optimization problems. The proposed method facilitates an even classification of time-series data into closely located clusters while maintaining robustness against outliers. We compared the proposed method with an existing standard method for clustering an online distributed dataset and found that both methods yielded comparable results. Furthermore, the proposed method was applied to a flow measurement image dataset containing noticeable noise with a signal-to-noise ratio of approximately unity. Despite a small signal variation of approximately 2%, the proposed method effectively classified the data without any overlaps among the clusters. In contrast, the clustering results of the existing methods exhibited overlapping clusters. These results indicate the effectiveness of the proposed method.

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confidence: 99%