Abstract
Analyzing the properties of subsequences within time series can reveal hidden patterns and improve the quality of time series clustering. However, most existing methods for subsequence analysis require point-to-point alignment, which is sensitive to shifts and noise. In this paper, we propose a clustering method named CTDS that treats time series as a set of independent and identically distributed (iid) points in \(\mathbb {R}^d\) extracted by a sliding window in local regions. CTDS utilises a distributional measure called Isolation Distributional Kernel (IDK) that can capture the subtle differences between probability distributions of subsequences without alignment. It has the ability to cluster large non-stationary and complex datasets. We evaluate CTDS on UCR time series benchmark datasets and demonstrate its superior performance than other state-of-the-art clustering methods.
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Notes
- 1.
The source code is available at https://github.com/LeisureGong/CTDS.
- 2.
The training and testing subsets of each dataset are merged for clustering evaluation.
- 3.
For short time series data with length less than 150, we directly generate all subsequences without split the data into segments.
- 4.
We also evaluated their performance using RI and found a similar result. All experimental details can be found in the supplementary file.
- 5.
For methods that solely produce representations or kernel matrices, we use K-means or Kernel K-means as the clustering algorithm.
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This project is supported by National Natural Science Foundation of China (Grant No. 62076120).
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Gong, L., Zhang, H., Liu, Z., Ting, K.M., Cao, Y., Zhu, Y. (2024). Local Subsequence-Based Distribution for Time Series Clustering. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_21
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