Abstract
With an objective of discovering compact features of unlabeled time series data and assessing the performance of grouping homogeneous patterns, this paper thus propose a mathematical modeling that could be capable of the general problem of multi-label time series clustering with high accuracy and reliability. The proposed method is an extension of original Kalman Filter algorithm that offers its main advantages by handling the complex characteristic of time series including: noise, temporal dynamics, correlation, time-lags, and harmonics. Consequently, the vital low-rank features extracted from the proposed approach have benefit properties including comprehensible, interpretable and visualizable that boost clustering quality. The experimental result on real-world dataset is presented to verify the contributions of the proposed method via improving significant performance compared with well-known competitors in both terms of its effectiveness and scalability.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B4011409). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057440). This research is funded by Funds for Science and Technology Development of the University of Danang under project number B2017-ĐN03-07.
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Nguyen, N.A.T., Vo, T.H., Kim, SH., Nguyen, T.Q.V., Oh, AR., Yang, HJ. (2018). Low-Feature Extraction for Multi-label Patterns Analyzing in Complex Time Series Mining. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_29
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DOI: https://doi.org/10.1007/978-3-319-75417-8_29
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