[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3745/JIPS.01.0040

Incremental Fuzzy Clustering Based on a Fuzzy Scatter Matrix

Liu, Yongli (School of Computer Science and Technology, Henan Polytechnic University)
Wang, Hengda (School of Computer Science and Technology, Henan Polytechnic University)
Duan, Tianyi (School of Computer Science and Technology, Henan Polytechnic University)
Chen, Jingli (School of Computer Science and Technology, Henan Polytechnic University)
Chao, Hao (School of Computer Science and Technology, Henan Polytechnic University)

Publication Information

Journal of Information Processing Systems / v.15, no.2, 2019 , pp. 359-373 More about this Journal

Abstract

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

Keywords

Fuzzy Clustering; Incremental Clustering; Scatter Matrix;

Citations & Related Records

Reference

1	S. Young, I. Arel, T. P. Karnowski, and D. Rose, "A fast and stable incremental clustering algorithm," in Proceedings of 2010 7th International Conference on Information Technology: New Generations (ITNG), Las Vegas, NV, 2010, pp. 204-209.
2	S. Chakraborty and N. K. Nagwani, "Analysis and study of incremental k-means clustering algorithm," High Performance Architecture and Grid Computing. Heidelberg: Springer, 2011, pp. 338-341.
3	L. E. Aik and T. W. Choon, "An incremental clustering algorithm based on Mahalanobis distance," in AIP Conference Proceedings, vol. 1635, pp. 788-793, 2014.
4	J. P. Mei, Y. Wang, L. Chen, and C. Miao, "Incremental fuzzy clustering for document categorization," in Proceedings of 2014 IEEE International Conference on Fuzzy Systems, Beijing, China, 2014, pp. 1518-1525.
5	Y. Wang, L. Chen, and J. P. Mei. "Incremental fuzzy clustering with multiple medoids for large data," IEEE Transactions on Fuzzy Systems, vol. 22, no. 6, pp. 1557-1568, 2014. DOI
6	M. F. K. Minhas, R. A. Abbasi, N. R. Aljohani, A. A. Albeshri, M. Mushtaq, "INTWEEMS: a framework for incremental clustering of tweet streams," in Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, Brussels, Belgium, 2015.
7	L. Pradeep and A. M. Sowjanya, "Multi-density based incremental clustering," International Journal of Computer Applications, vol. 116, no. 17, pp. 6-9, 2015. DOI
8	O. Shmueli and L. Shnaiderman, "Incremental clustering of indexed XML data," U.S. Patent 8930407, Jan 6, 2015.
9	F. Cambi, P. Crescenzi, and L. Pagli, "Analyzing and comparing on-line news sources via (two-layer) incremental clustering," in Proceedings of the 8th International Conference on Fun with Algorithms, La Maddalena, Italy, 2016.
10	L. Chen, M. Liu, C. Wu, and A. Xu, "A novel clustering algorithm and its incremental version for largescale text collection," Information Technology and Control, vol. 45, no. 2, pp. 136-147, 2016.
11	D. Wang and A. H. Tan, "Self-regulated incremental clustering with focused preferences," in Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 2016, pp. 1297-1304.
12	P. Hore, L. O. Hall, and D. B. Goldgof, "Single pass fuzzy c means," in Proceedings of 2007 International Fuzzy Systems Conference, London, UK, 2007, pp. 1-7.
13	P. Hore, L. O. Hall, D. B. Goldgof, and W. Cheng, "Online fuzzy c means," in Proceedings of 2008 Annual Meeting of the North American Fuzzy Information Processing Society, New York, NY, 2008, pp. 1-5.
14	A. Maratea, A. Petrosino, and M. Manzo, "Adjusted F-Measure and kernel scaling for imbalanced data learning," Information Sciences, vol. 257, pp. 331-341, 2014. DOI
15	R. Clausius, "Ueber verschiedene fur die Anwendung bequeme Formen der Hauptgleichungen der mechanischen Warmetheorie," Annalen der Physik, vol. 201, no. 7, pp. 353-400, 1865. DOI
16	J. C. Bezdek, R. Ehrlich, and W. Full, "FCM: the fuzzy c-means clustering algorithm," Computers & Geosciences, vol. 10, no. 2-3, pp. 191-203, 1984. DOI
17	Y. Liu and X. Wan, "Information bottleneck based incremental fuzzy clustering for large biomedical data," Journal of Biomedical Informatics, vol. 62, pp. 48-58, 2016. DOI
18	A. K. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, no. 8, pp. 651-666, 2009. DOI
19	Y. Zhou, H. F. Zuo, and J. Feng, "A clustering algorithm based on feature weighting fuzzy compactness and separation," Algorithms, vol. 8, no. 2, pp. 128-143, 2015. DOI
20	K. L. Wu, J. Yu, and M. S. Yang, "A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests," Pattern Recognition Letters, vol. 26, no. 5, pp. 639-652, 2005. DOI
21	C. Y. Chen, S. C. Hwang, and Y. J. Oyang, "An incremental hierarchical data clustering algorithm based on gravity theory," in Advances in Knowledge Discovery and Data Mining. Heidelberg: Springer, 2002, pp. 237-250.