Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

• DOI: 10.1371/journal.pone.0197499
• Publication Date: 2018-05-24T18:09:18Z
• Authors: Yongli Liu; Jingli Chen; Shuai Wu; Zhizhong Liu; Hao Chao
• Keywords: Dynamic Time Warping; Benchmark (surveying)

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for may help improve the health level people. Considering scale shifts series, this paper, we introduce two incremental fuzzy based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass Online patterns, our handle large-scale by splitting into set chunks which are processed sequentially. Besides, select DTW to measure distance pair-wise encourage higher accuracy because determine an optimal match between any stretching or compressing segments temporal data. Our new compared some existing prominent 12 benchmark datasets. The experimental results show that proposed approaches yield high quality clusters were better than all competitors terms accuracy.