1. Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance
- Author
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Jingli Chen, Liu Zhizhong, Liu Yongli, Chao Hao, and Wu Shuai
- Subjects
Time Factors ,Computer science ,Entropy ,lcsh:Medicine ,02 engineering and technology ,Distance Measurement ,Electrocardiography ,Mathematical and Statistical Techniques ,0202 electrical engineering, electronic engineering, information engineering ,Data Mining ,Cluster Analysis ,lcsh:Science ,Measurement ,Multidisciplinary ,Data Processing ,Applied Mathematics ,Simulation and Modeling ,Physics ,Medoid ,Temporal database ,Bioassays and Physiological Analysis ,Physical Sciences ,Thermodynamics ,Engineering and Technology ,020201 artificial intelligence & image processing ,Information Technology ,Algorithms ,Statistics (Mathematics) ,Research Article ,Dynamic time warping ,Computer and Information Sciences ,Fuzzy clustering ,Research and Analysis Methods ,Fuzzy logic ,Clustering Algorithms ,Fuzzy Logic ,020204 information systems ,Entropy (information theory) ,Time series ,Statistical Methods ,Cluster analysis ,business.industry ,Electrophysiological Techniques ,lcsh:R ,Pattern recognition ,Models, Theoretical ,ComputingMethodologies_PATTERNRECOGNITION ,lcsh:Q ,Artificial intelligence ,Cardiac Electrophysiology ,business ,Mathematics ,Forecasting - Abstract
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 time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.
- Published
- 2018