Abstract
Clustering time series data is frequently hampered by various noise components within the signal. These disturbances affect the ability of clustering to detect similarities across the various signals, which may result in poor clustering results. We propose a method, which first smooths out such noise using wavelet decomposition and thresholding, then reconstructs the original signal (with minimised noise) and finally undertakes the clustering on this new signal. We experimentally evaluate the proposed method on 250 signals that are generated from five classes of signals. Our proposed method achieves improved clustering results.
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Notes
- 1.
Given we know the class that a signal belongs to, we first ignore the class label of a signal to simulate a real life clustering scenario where the class labels are unknown. After clustering the signals we use the class labels as ground truth for the purpose of clustering evaluation.
- 2.
Clustering result tables for Simple Moving Average order 3, Hard and Soft thresholding not shown, similarly for clustering using raw wavelet coefficients.
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Grant, P., Islam, M.Z. (2019). Clustering Noisy Temporal Data. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_13
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