Exploring the Depths of K-Means Clustering: A Critical Analysis

Authors

  • P. S. Deshmukh  Ph. D. Scholar of Dept of CSE, SRK, University, Bhopal, Madhya Pradesh, India
  • Dr. M. Sivakkumar  Associate Professor in Dept of CSE, SRK University, Bhopal, Madhya Pradesh, India
  • Dr. Varshaha Namdeo  Professor in Dept of CSE, SRK University, Bhopal, Madhya Pradesh, India

Keywords:

Image Segmentation, K-Means Clustering, Fuzzy C-Means Clustering, Centroids

Abstract

In image segmentation, clustering is the process of sub dividing the whole image into the meaningful sub images. The most commonly used image segmentation algorithms such as K-means and Fuzzy c-means clustering face the specific important problem in selecting the optimal number of clusters and the corresponding cluster centroids. Plenty of research works have been done on the limitations of the said clustering algorithms to improve the efficient isolation of clusters. This paper enumerates the works done by different researchers in selecting the initial number of clusters and the centroids using K-means and Fuzzy c-means clustering. The limitations and applications of the above-mentioned clustering algorithms are explored.

References

  1. X. Cufí, X. Muñoz, J. Freixenet and J. Martí, “A review of image segmentation techniques integrating region and boundary information,” Advances in Imaging and Electron Physics, Elsevier, vol.120, 2003, pp 1-39.
  2. A. Pugazhenthi and L. S. Kumar, "Selection of Optimal Number of Clusters and Centroids for K-means and Fuzzy C-means Clustering: A Review," 2020 5th International Conference on Computing, Communication and Security (ICCCS), 2020, pp. 1-4, doi: 10.1109/ICCCS49678.2020.9276978.
  3. Gonzalez R. C., Woods R. E., Digital Image Processing, 4th edition, Pearson Education, 2018.
  4. A. Pugazhenthi and J. Singhai, “Automatic centroids selection in Kmeans clustering based image segmentation,” 2014 International Conference on Communication and Signal Processing,
  5. Melmaruvathur, 2014, pp. 1279-1284.
  6. A. Pugazhenthi, G. Sreenivasulu and A. Indhirani, “Background removal by modified fuzzy C-means clustering algorithm,” 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2015, pp. 1-3.
  7. M. R. Rezaee, P. M. J. van der Zwet, B. P. E. Lelieveldt, R. J. van der Geest and J. H. C. Reiber, “A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering,” IEEE Transactions on Image Processing, vol. 9, no. 7, July 2000, pp. 1238-1248.
  8. A. M. Fahim, A. M. Salem and F. A. Torkey, “An efficient enhanced k-means clustering algorithm,” Journal of Zhejiang UniversitySCIENCE A, vol. 7, no.10, 2006, pp. 1626–1633.
  9. A. Bhattacharya and R. K. De, “Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles,” Bioinformatics,vol.24, no.11, 2008, pp. 1359 1366.
  10. M. J. Li, M. K. Ng, Y. Cheung and J. Z. Huang, “Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 11, Nov. 2008, pp. 1519-1534.
  11. S. N. Sulaiman and N. A. Mat Isa, “Adaptive fuzzy-K-means clustering algorithm for image segmentation,” IEEE Transactions on Consumer Electronics, vol. 56, no. 4, Nov 2010, pp. 2661-2668.
  12. U. Maulik and I. Saha, “Automatic Fuzzy Clustering Using Modified Differential Evolution for Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 9, Sep 2010, pp. 3503-3510. 
  13. B. Yi, H. Qiao, F. Yang and C. Xu, “An Improved Initialization Center Algorithm for K-Means Clustering,” 2010 International Conference on Computational Intelligence and Software Engineering, Wuhan, 2010, pp. 1-4.
  14. F. Bu, Z. Chen, Q. Zhang and X. Wang, "Incomplete Big Data Clustering Algorithm Using Feature Selection and Partial Distance," 2014 5th International Conference on Digital Home, Guangzhou, China, 2014, pp. 263-266.
    doi: 10.1109/ICDH.2014.57
  15. V. T. N. Chau, N. H. Phung and V. T. N. Tran, "A robust and effective algorithmic framework for incomplete educational data clustering," 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), Ho Chi Minh City, Vietnam, 2015, pp. 65-70.doi: 10.1109/NICS.2015.7302224\
  16. S. Hua-Yan, L. Ye-Li, Z. Yun-Fei and H. Xu, "Accelerating EM Missing Data Filling Algorithm Based on the K-Means," 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, China, 2018, pp. 401-406.
    doi: 10.1109/ICNISC.2018.00088
  17. V. T. N. Chau, P. H. Loc and V. T. N. Tran, "A Robust Mean Shift-Based Approach to Effectively Clustering Incomplete Educational Data," 2015 International Conference on Advanced Computing and Applications (ACOMP), Ho Chi Minh City, Vietnam, 2015, pp. 12-19.doi: 10.1109/ACOMP.2015.14
  18. S. Wang et al., "K-Means Clustering With Incomplete Data," in IEEE Access, vol. 7, pp. 69162-69171, 2019.
    doi: 10.1109/ACCESS.2019.2910287
  19. H. Liu, J. Wu, T. Liu, D. Tao and Y. Fu, "Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence," in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 5, pp. 1129-1143, 1 May 2017.doi: 10.1109/TKDE.2017.2650229
  20. V. T. Ngoc Chau, "A Robust Self-Organizing Approach to Effectively Clustering Incomplete Data," 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), Ho Chi Minh City, Vietnam, 2015, pp. 150-155.doi: 10.1109/KSE.2015.11
  21. K. Honda, R. Nonoguchi, A. Notsu and H. Ichihashi, "PCA-guided k-Means clustering with incomplete data," 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, Taiwan, 2011, pp. 1710-1714.doi: 10.1109/FUZZY.2011.6007312
  22. M. Vasuki and S. Revathy, "Efficient Handling of Incomplete basic Partitions by Spectral Greedy K-Means Consensus Clustering," 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2020, pp. 299-305.doi: 10.1109/ICCMC48092.2020.ICCMC-00056

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P. S. Deshmukh, Dr. M. Sivakkumar, Dr. Varshaha Namdeo, " Exploring the Depths of K-Means Clustering: A Critical Analysis " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.340-349, May-June-2021.