Clustering time series data is frequently hampered by various noise components within the signal. These disturbances aect the ability of clustering to detect similarities across the various signals, which may result in poor clustering results. We propose a method, which rstsmooths out such noise using wavelet decomposition and thresholding, then reconstructs the original signal (with minimised noise) and nallyundertakes the clustering on this new signal. We experimentally evaluate the proposed method on 250 signals that are generated from ve classes of signals. Our proposed method achieves improved clustering results.
Original languageEnglish
Title of host publication15th International Conference on Advanced Data Mining and Applications (ADMA 2019)
Number of pages9
ISBN (Electronic)9783030352318
ISBN (Print)9783030352301
Publication statusPublished - Nov 2019
Event15th International Conference on Advanced Data Mining and Applications 2019: ADMA 2019 - Hi Chance (Dalian) Science & Technology Center, Dalian, China
Duration: 21 Nov 201923 Nov 2019
https://web.archive.org/web/20191216185322/http://adma2019.neusoft.edu.cn/ (Conference website)
https://link.springer.com/book/10.1007/978-3-030-35231-8 (proceedings)

Publication series

NameLecture Notes in Computer Science


Conference15th International Conference on Advanced Data Mining and Applications 2019
OtherThe conference aims at bringing together the experts on data mining from around the world, and providing a leading international forum for the dissemination of original research findings in data mining, spanning applications, algorithms, software and systems, as well as different applied disciplines with potential in data mining, such as smartphone and social network mining, bio-medical science and green computing. ADMA 2019 will promote the same close interaction and collaboration among practitioners and researchers. Published papers will go through a full peer review process.
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