The unordered time series fuzzy clustering algorithm based on the adaptive incremental learning

• Published in: Journal of intelligent & fuzzy systems Vol. 38; no. 4; pp. 3783 - 3791
• Main Authors: Xu, Huanchun, Hou, Rui, Fan, Jinfeng, Zhou, Liang, Yue, Hongxuan, Wang, Liusheng, Liu, Jiayue
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 30.04.2020
• Subjects: Adaptive algorithms; Algorithms; Cluster analysis; Clustering; Euclidean geometry; Machine learning; Segmentation; Time series
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-179601

The data of time series are massive in quantity and not conducive to subsequent processing. Therefore, the unordered time series fuzzy clustering algorithm of adaptive incremental learning has been utilized to explore the segmentation of time series in further. The research results show that the emergence of incremental learning technology can solve such problems. Also, it can continuously accumulate and increase the data, as well as improving the learning accuracy. Incremental learning technology correctly processes, retains, and utilizes the historical results, thereby reducing the training time of new samples by using historical results. Therefore, the clustering algorithm mostly clusters the cluster-liked shape of discrete datasets and uses the hierarchical clustering algorithm, which is more suitable for measuring the similarity of time series, to replace the Euclidean distance for distance metric and hierarchical clustering. The distance matrix update method is improved to reduce the computational complexity, which proves that the algorithm has higher clustering validity and reduces the operating time of the algorithm.