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所属单位：School of Reliability and Systems Engineering, Beihang University, Beijing, China
发表刊物：IEEE Transactions on Neural Networks and Learning Systems
关键字：Clustering by fast search (CFS), clusters adjustment, incremental clustering, large data, multiplere
摘要：With the prevailing development of Cyber-physicalsocial systems and Internet of Things, large-scale data have been collected consistently. Mining large data effectively and efficiently becomes increasingly important to promote the development and improve the service quality of these applications. Clustering, a popular data mining technique, aims to identify underlying patterns hidden in the data. Most clustering methods assume the static data, thus they are unfavorable for analyzing large, unbalanced dynamic data. In this paper, to address this concern, we focus on incremental clustering by ext
合写作者：Z. Chen,Liang Zou,Z. Jane Wang
第一作者：L. Zhao
论文类型：开发研究
通讯作者：杨懿
一级学科：控制科学与工程
文献类型：期刊
页面范围：1-11
ISSN号：21622388 2162237X
是否译文：否
CN号：null
发表时间：2018-07-25
收录刊物：SCI
发布期刊链接：https://doi-org-443.e.buaa.edu.cn/10.1109/TNNLS.2018.2851979