We would like to thank the anonymous reviewers for their suggestions to improve this article. This work was supported by National Natural Science Foundation of China (61003130), National Science and Technology Support Program (2012BAH33F03) and Natural Science Foundation of Hubei Province (2015CFB525). Li Lin was partially supported by an award from the Wuhan Science and Technology Innovation Team Project (201307020402005).
机构署名：
本校为其他机构
院系归属：
机械工程学院
摘要：
To address the problem of the difficulty of traditional clustering methods to adapt to online clustering of streaming data and on the basis of the research on the evolutionary clustering method (ECM), this paper proposes a Davies-Bouldin index evolving clustering method for streaming data clustering (DBIECM). This method has improved the updating process of the clustering center and the radius of ECM and introduced the Davies-Bouldin Index (DBI) as the evaluation criterion for data classification. Compared with the traditionalclustering method,...
