Abstract
Time series clustering is a key unsupervised data mining technique that has been widely applied in various domains for discovering patterns, insights and applications. Extracting meaningful features from time series is crucial for clustering. However, most existing feature extraction algorithms fail to capture the complex and dynamic patterns in time series data. In this paper, we propose a novel kernel-based feature extraction algorithm that utilizes a data-dependent kernel function with an efficient dimensionality reduction method. Our algorithm can adapt to the local data distribution and represent high-frequency subsequences of time series effectively. We demonstrate that, with a bag-of-words model, our feature extraction algorithm outperforms other existing methods for time series clustering on many real-world datasets from various domains.
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References
Baum, C.F., Schaffer, M.E., Stillman, S.: Instrumental variables and GMM: estimation and testing. Stand. Genom. Sci. 3(1), 1–31 (2003)
Destefanis, G., Barge, M.T., Brugiapaglia, A., Tassone, S.: The use of principal component analysis (PCA) to characterize beef. Meat Sci. 56(3), 255–259 (2000)
Dvornik, M., Hadji, I., Derpanis, K.G., Garg, A., Jepson, A.: Drop-DTW: aligning common signal between sequences while dropping outliers. Adv. Neural. Inf. Process. Syst. 34, 13782–13793 (2021)
Hatami, N., Gavet, Y., Debayle, J.: Bag of recurrence patterns representation for time-series classification. Pattern Anal. Appl. 22(3), 877–887 (2019)
Hout, M.C., Papesh, M.H., Goldinger, S.D.: Multidimensional scaling. Wiley Interdisc. Rev. Cogn. Sci. 4(1), 93–103 (2013)
Klaser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC 2008–19th British Machine Vision Conference, pp. 275–281. British Machine Vision Association (2008)
Linardi, M., Palpanas, T.: Scalable, variable-length similarity search in data series: the Ulisse approach. Proc. VLDB Endow. 11(13), 2236–2248 (2018)
Paparrizos, J., Gravano, L.: k-shape: Efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1855–1870 (2015)
Passalis, N., Tsantekidis, A., Tefas, A., Kanniainen, J., Gabbouj, M., Iosifidis, A.: Time-series classification using neural bag-of-features. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 301–305. IEEE (2017)
Pérez, S.I., Moral-Rubio, S., Criado, R.: A new approach to combine multiplex networks and time series attributes: building intrusion detection systems (IDS) in cybersecurity. Chaos Solitons Fract. 150, 111143 (2021)
Qin, X., Ting, K.M., Zhu, Y., Lee, V.C.: Nearest-neighbour-induced isolation similarity and its impact on density-based clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4755–4762 (2019)
Sarfraz, S., Koulakis, M., Seibold, C., Stiefelhagen, R.: Hierarchical nearest neighbor graph embedding for efficient dimensionality reduction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 336–345 (2022)
Ting, K.M., Xu, B.C., Washio, T., Zhou, Z.H.: Isolation distributional kernel: a new tool for kernel based anomaly detection. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 198–206 (2020)
Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11(Oct), 2837–2854 (2010)
Wang, H., Li, C., Sun, H., Guo, Z., Bai, Y.: Shapelet classification algorithm based on efficient subsequence matching. Data Sci. J. 17 (2018)
Wang, J., McDonald, N., Cochran, A.L., Oluyede, L., Wolfe, M., Prunkl, L.: Health care visits during the COVID-19 pandemic: a spatial and temporal analysis of mobile device data. Health and Place 72, 102679 (2021)
Wang, Y., Perry, M., Whitlock, D., Sutherland, J.W.: Detecting anomalies in time series data from a manufacturing system using recurrent neural networks. J. Manuf. Syst. 62, 823–834 (2022)
Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956 (2009)
Zhang, M., Sawchuk, A.A.: Motion primitive-based human activity recognition using a bag-of-features approach. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 631–640 (2012)
Zhao, J., Itti, L.: Classifying time series using local descriptors with hybrid sampling. IEEE Trans. Knowl. Data Eng. 28(3), 623–637 (2015)
Zhao, J., Itti, L.: shapeDTW: shape dynamic time warping. Pattern Recogn. 74, 171–184 (2018)
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Liu, Y. et al. (2023). Kernel-Based Feature Extraction for Time Series Clustering. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_24
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DOI: https://doi.org/10.1007/978-3-031-40283-8_24
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