Browse > Article
http://dx.doi.org/10.7471/ikeee.2019.23.2.419

Improved TI-FCM Clustering Algorithm in Big Data  

Lee, Kwang-Kyug (IT Convergence Engineering, Shinhan University)
Publication Information
Journal of IKEEE / v.23, no.2, 2019 , pp. 419-424 More about this Journal
Abstract
The FCM algorithm finds the optimal solution through iterative optimization technique. In particular, there is a difference in execution time depending on the initial center of clustering, the location of noise, the location and number of crowded densities. However, this method gradually updates the center point, and the center of the initial cluster is shifted to one side. In this paper, we propose a TI-FCM(Triangular Inequality-Fuzzy C-Means) clustering algorithm that determines the cluster center density by maximizing the distance between clusters using triangular inequality. The proposed method is an effective method to converge to real clusters compared to FCM even in large data sets. Experiments show that execution time is reduced compared to existing FCM.
Keywords
Fuzzy C-Means(FCM); K-Means; Data Mining; Big Data; Clustering;
Citations & Related Records
연도 인용수 순위
  • Reference
1 http://www-01.ibm.com/software/data/bigdata
2 Mugdha Jain, Chakradhar Verma, "Adapting k-means for Clustering in Big Data," International Journal of Computer Applications (0975-8887), Vol.101, No.1, 2014. DOI: 10.5120/17652-8457   DOI
3 The Big Data Long Tail. Blog post by Bloomberg, Jason. 2013.
4 Soumi Ghosh, Sanjay Kumar Dubey, "Comparative Analysis of K-Means and Fuzzy C-Means Algorithms,"(IJACSA) International Journal of Advanced Computer Science and Applications, Vol.4, No.4, 2013. DOI: 10.14569/IJACSA.2013.040406   DOI
5 Fahad, A, Alshatri, N., Tari, Z., AlAmri, A., Zomaya, Y., Khalil, I., Foufou, S., Bouras, A, "A Survey of Clustering Algorithms for Big Data: Taxonomy & Empirical Analysis," Emerging Topics in Computing, IEEE Transactions, vol.PP, no.99, pp.1, 1. 2014. DOI: 10.1109/TETC.2014.2330519
6 Anwesha Barai (Deb), Lopamudra Dey, "Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering," World Journal of Computer Application and Technology, Vol.5, No.2, pp.24-29, 2017. DOI: 10.13189/wjcat.2017.050202   DOI
7 J. Bezdek, Pattern Recognition with fuzzy Objective Function Algorithms, New York, Springer, 1981.
8 Christopher, T., and T. Divya. "A Study of Clustering Based Algorithm for Outlier Detection in Data streams," Proceedings of the UGC Sponsored National Conference on Advanced etworking and Applications. 2015.
9 S. Muyamoto, Fuzzy Clustering-Basic Ideas and Overview, Handbook of Computational Intelligence, Springer, pp.293-248, 2015.
10 J. Nayak, "Fuzzy C-means(FCM) Clustering Algorithm: A Decade Review from 2000 to 2014," Systems and Technologies, vol.32, no.2, pp.133-179, 2014.