The week's pick
Reseach Article
Article:Comparative analysis of FCM and HCM algorithm on Iris data set
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 2 |
| Year of Publication: 2010 |
| Authors: Deepika Sirohi, Pawan Kumar |
10.5120/888-1261
|
Deepika Sirohi, Pawan Kumar . Article:Comparative analysis of FCM and HCM algorithm on Iris data set. International Journal of Computer Applications. 5, 2 ( August 2010), 33-37. DOI=10.5120/888-1261
Abstract
Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. There are various algorithms used to solve this problem. This paper presents the comparison of the performance analysis of Fuzzy C mean (FCM) clustering algorithm and compares it with Hard C Mean (HCM) algorithm on Iris flower data set. We measure Time complexity and space Complexity of FCM and HCM at Iris data [1] set. FCM clustering [2, 3] is a clustering technique which is separated from Hard C Mean that employs hard partitioning. The FCM employs fuzzy portioning such that a point can belong to all groups with different membership grades between 0 and 1.
References
- Wei Wang, Chunheng Wang, Xia Cui, Ai Wang, “A Clustering Algorithm Combine the FCM algorithm with Supervised Learning Normal Mixture Model”, IEEE 2008.
- Deepak Agrawal “Web Data Clustering using FCM and Proximity Hints from Text as well as Hyperlink-structure”, IEEE 2008.
- M. Brej and M. Sonka, “Object localization and border detection criteria design in edge-based image segmentation automated learning from examples”, IEEE Transactions on Medical imaging, vol. 19, pp. 973-985, 2000.
- S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure”, IEEE Transactions on Systems, Man and Cybernetics, vol. 34, pp. 1907-1916, 1998.
- O. Sojodishijani, V. Rostami and A. R. Ramli, “Real time color image segmentation with non-symmetric Gaussian membership functions”, Fifth International Conference on Computer Graphics, Imaging and Visualization, pp. 165-170, 2008.
- M. S. Yanp, K.L. Wu and J. Yub, “A novel fuzzy clustering algorithm”, IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol. 2, pp. 647- 652, 2003.
- L. Hui, “Method of image segmentation on high-resolution image and classification for land covers”, Fourth International Conference on Natural Computation, vol. 5, pp. 563-566, 2008.
- D. L. Pham, “Spatial models for fuzzy clustering”, Laboratory of Personality and Cognition, Gerontology Research Center, 2001.
- R. J. Almeida and J. M. C. Sousa, “Comparison of fuzzy clustering algorithms for Classification”, International Symposium on Evolving Fuzzy Systems, pp. 112-117, 2006.
- M. Alata, M. Molhim, and A. Ramini, “Optimizing Fuzzy C Means clustering algorithm using GA”, Proceedings of World Academy of Science, Engineering and Technology, vol. 29, 2008.
- Prodip Hore, Lawrence O. Hall, and Dmitry B. Goldgof “Single Pass Fuzzy C Means”, CSEEE, vol. 28, 2000.
- T. Saegusa and T. Maruyama, “Real-time segmentation of color images based on the k-Means clustering on FPGA”, International Conference on Field-Programmable Technology, pp. 329-33, 2007.
- P. F. Felzenszwalb, D. P. Huttenlocher, “Efficient graph-based image segmentation”, International Journal of Computer Vision, vol. 59, pp. 167 – 181, 2004.
- H. Ichihashi, K. Honda, N. Kuwamoto and Takao Hattori, “Post-supervised Fuzzy C means classifier with hard clustering”, Proceedings of IEEE Symposium on Computational Intelligence and Data Mining, 2007.
Index Terms
Keywords