Clustering matrix sequences based on the iterative dynamic time deformation procedure

Clustering matrix sequences based on the iterative dynamic time deformation procedure

Автор: Zhengbing Hu, Sergii V. Mashtalir, Oleksii K. Tyshchenko, Mykhailo I. Stolbovyi

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 7 vol.10, 2018 года.

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The techniques of Dynamic Time Warping (DTW) have shown a great efficiency for clustering time series. On the other hand, it may lead to sufficiently high computational loads when it comes to processing long data sequences. For this reason, it may be appropriate to develop an iterative DTW procedure to be capable of shrinking time sequences. And later on, a clustering approach is proposed for the previously reduced data (by means of the iterative DTW). Experimental modeling tests were performed for proving its efficiency.

Time Series, Segmentation, Clustering, Video Stream, Proximity Measure, Dynamic Time Warping

Короткий адрес: https://sciup.org/15016509

IDR: 15016509   |   DOI: 10.5815/ijisa.2018.07.07

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