DBIECM-an Evolving Clustering Method for Streaming Data Clustering

DBIECM-an Evolving Clustering Method for Streaming Data Clustering

Kai-song Zhang* Luo Zhong Lan Tian Xuan-ya Zhang Lin Li

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China

Department of Electronic Information Engineering, Wuhan City Vocational College, Wuhan 430064, China

Corresponding Author Email: 
15 March 2017
15 April 2017
31 March 2017
| Citation



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 traditional clustering method, DBIECM has better adaptability for stream data clustering. The experiments show that DBIECM has a better clustering effect on the evaluation criteria of the objective function value, DBI, as well as better accuracy and purity compared with ECM.


Streaming data, Online clustering, Evolving clustering, Davies-Bouldin Index

1. Introduction
2. Evolving Clustering Method
3. DBIECM for Streaming Data Clustering
4. Experimental Results and Analysis
5. Conclusion

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