Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

• Published in: Journal of computer science and technology Vol. 30; no. 4; pp. 859 - 873
• Main Author: 周竞 朱山风 黄晓地 张彦春
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2015; Springer Nature B.V
• Subjects: Algorithms; Approximation; Artificial Intelligence; Clustering; Computer Science; Data Structures and Information Theory; Datasets; Dynamics; Information Systems Applications (incl.Internet); Laboratories; Learning; Methods; Questions; Regular Paper; Semi-supervised learning; Similarity measures; Software Engineering; Spectra; Studies; Theory of Computation; Time series; Warpage; Warping; 动态时间归整; 学习方法; 数据集; 时间序列谱; 测量性能; 监督; 编辑距离; 聚类方法; United States > US; time series analysis; dynamic programming; clustering; information search and retrieval

Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping （DTW） based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the f...