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: 
zkspr@whut.edu.cn
Page: 
239-254
|
DOI: 
https://doi.org/10.18280/ama_b.600115
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

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.

Keywords: 

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
Acknowledgements
  References

1. J. Hou, W. Liu, E. Xu, H. Cui, Towards parameter-independent data clustering and image segmentation, 2016, Pattern Recognition, vol. 60, pp. 25-36.

2. J.A. Hartigan, M.A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, 1979, Applied Statistics, vol. 28, no.1, pp. 100-108.

3. Y.K. Dubey, M.M. Mushrif, FCM Clustering Algorithms for Segmentation of Brain MR Images, 2016, Advances in Fuzzy System, no. 1, pp. 1-14.

4. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: A New Data Clustering Algorithm and Its Applications, 1997, Data Mining and Knowledge Discovery, vol. 1, no. 2, pp. 141-182.

5. C. Zhang, J. Zhang, Learning on Time-Evolving Data, 2013, Chinese Journal of Computers, vol. 36, no. 2, pp. 310-316.

6. Q. Song, N. Kasabov, ECM-A Novel On-line, Evolving Clustering Method and Its Applications, 2002, M.i.posner Foundations of Cognitive Science, pp. 631-682.

7. N.K. Kasabov, Q. Song, DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction, 2002, IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 144–154.

8. D. Chakrabarti, R. Kumar, A. Tomkins, Evolutionary clustering, 2011, In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 554–560.

9. E.G. Mansoori, FRBC: A Fuzzy Rule-Based Clustering Algorithm, 2011, IEEE Transactions on Fuzzy Systems, vol. 19, no. 5, pp. 960-971.

10. V. Ravi, E.R. Srinivas, N.K. Kasabov, On-line Evolving Fuzzy Clustering, 2007, International Conference on Computational Intelligence and Multimedia Applications, vol. 1, pp. 347-351.

11. G.Y. Du, S.L. Tian, F. Miao, Remote sensing image segmentation based on evolving clustering and fuzzy C-means, 2009, Application Research of Computers, vol. 26, no. 2, pp 3995-3997.

12. L. Wang, H. Sun, Evolving clustering method based on self-adaptive learning, 2016, Control and Decision, vol. 31, no. 03, pp. 423-428.

13. S. Abdulla, A. Al-Nassiri, kEFCM: kNN-Based Dynamic Evolving Fuzzy Clustering Method, 2015, International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, no. 2, pp. 5-13.

14. D.L. Davies, D.W. Bouldin, A Cluster Separation Measure, 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224-227.

15. Y. Sun, Q.M. Zhu, Z.X. Chen, An iterative initial-points refinement algorithm for categorical data Clustering, 2002, Pattern Recognition Letters, vol. 23, no. 7, pp. 875-884.

16. J.Y. Chen, H.H. He, A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data, 2016, Information Sciences An International Journal,  vol. 345, no. C, pp. 271-293.

17. J.C. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means clustering algorithm, 1984, Computers & Geosciences, vol. 10, no. 2-3, pp. 191-203.

18. T.K. Chang, A. Talei, S. Alaghmand, L.H.C. Chua, Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning, 2016, Procedia Engineering, vol. 154, pp. 1103-1109.

19. M.J. Inácio, R.D. Maia, W.M. Caminhas, Evolving Fuzzy Classifier Based on the Modified ECM Algorithm for Pattern Classification, 2012, Springer Berlin Heidelberg, vol. 7435, pp. 612-621.

20. X.L. Xie, G. Beni, A validity measure for fuzzy clustering, 1991, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 13, pp. 841-847.

21. A.K. Lekova, M.I. Dimitrova, Hand Gestures Recognition Based on Lightweight Evolving Fuzzy Clustering Method, 2013, Second International Conference on Image Information Processing, pp. 505-510.

22. P. Arora, Deepali, S. Varshney, Analysis of K-Means and K-Medoids Algorithm for Big Data, 2016, Procedia Computer Science, vol. 78, pp. 507-512.

23. R.G. Wei, J.G. Zhen, L.L. Bao, Study on mining big users data in the development of Hubei auto-parts enterprise, 2015, Mathematical Modelling of Engineering Problems, vol. 2, no. 4, pp. 1-6.

24. J. Xia, L. Xiao, L.P. Wan, Application of random-fuzzy probability statistics method, 2016, Mathematical Modelling of Engineering Problems, vol. 3, no. 1, pp. 19-24.

25. A.S.A. Hazmi, Z.A. Maurad, N.N.P.N. Pauzi, Z.A. Bakar, Z. Idris, Rapid evaluation of plate heat exchanger performance and fouling analysis in epoxidation of oleochemical at pilot plant scale, 2016, International Journal of Heat and Technology, vol. 34, no. 4, pp. 558-564.

26. M. Cucumo, V. Ferraro, D. Kaliakatsos, M. Mele, F. Nicoletti, Calculation model using finite-difference method for energy analysis in a concentrating solar plant with linear Fresnel reflectors, 2016, International Journal of Heat and Technology, vol. 34, Special Issue 2, pp. S337-S345.