影响因子：7.1
DOI码：10.1016/j.fss.2021.01.002
发表刊物：Fuzzy Sets and Systems
关键字：Fuzzy clustering; Incremental learning; Outliers; Time series
摘要：Clustering is one of the most popular data mining methods for analyzing the time series, not only due to its exploratory power, but also because it is often a preprocessing step or subroutine for other techniques. In this paper, an incremental fuzzy clustering algorithm of time series (IFCTS) is proposed, in which the clustering is divided into two stages: offline and online clustering. During the offline clustering process, the fuzzy clustering validity evaluation index is introduced to FCM to automatically obtain the optimal number of initial clusters for off-line data. Then, in the online clustering process, IFCTS algorithm can dynamically update the existing clusters in the incremental data snapshot, distinguish outliers and control the creation of new clusters adaptively by combining with the previous clusters. The experimental results show that the proposed algorithm has good clustering accuracy and efficiency for both equal-length and unequal-length time series.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：421
页面范围：62 - 76
ISSN号：01650114
是否译文：否
发表时间：2021-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0165011421000130