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Reseach Article

A Comparative Study on Clustering Algorithms using Image Data

by Vikas Tondar, Pramod S. Nair
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 17
Year of Publication: 2016
Authors: Vikas Tondar, Pramod S. Nair
10.5120/ijca2016908071

Vikas Tondar, Pramod S. Nair . A Comparative Study on Clustering Algorithms using Image Data. International Journal of Computer Applications. 133, 17 ( January 2016), 28-31. DOI=10.5120/ijca2016908071

@article{ 10.5120/ijca2016908071,
author = { Vikas Tondar, Pramod S. Nair },
title = { A Comparative Study on Clustering Algorithms using Image Data },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 17 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number17/24007-2016908071/ },
doi = { 10.5120/ijca2016908071 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:31:31.377750+05:30
%A Vikas Tondar
%A Pramod S. Nair
%T A Comparative Study on Clustering Algorithms using Image Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 17
%P 28-31
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Analyzing of image called Segmentation .It is an important concept to viewing and analyzing different type’s images and solving a wide range of problems in image. Clustering algorithm and technique for classifying usage image data and the process of analyze image data from dissimilar perception and abbreviation it into valuable information, this information can be use to increase proceeds, cuts costs, or Time complexity. There is different type of algorithms for image data and clustering such as (FCM) fuzzy c-means clustering algorithms, SFCM (Spatial fuzzy c-means clustering), K-Means, and PSOFCM (particle swarm optimization incorporative fuzzy c-means clustering) .The selection between the predictive classifier is extremely important. Fuzzy algorithms based on initial cluster selection without noise data. PSOFCM and SFCM approaches shows better segmentation results can be obtained in noise. PSOFCM and SFCM approaches shows how better image segmentation of results can be obtained. Image clustering and its applications are used in human image i.e. Medical image segmentation used for detection of Brain images, tumor and more. The result obtained through Particle swarm optimization (PSO), yields better detected image and time complexity compared to FCM and SFCM.

References
  1. J. Kennedy and R.C. Eberhart, "Particle swarm optimization" , Proceeding of the 1995 IEEE International Conference on Neural Networks (Perth,Australia), IEEE Service Centre, Piscataway, NI, (1995), Iv: 1942-1948 400
  2. . 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, 1998, pp. 1907-1916.
  3. A novel kernelized fuzzy c mean algorithms with application in medical science image segmentation ,Dao-Qiang Zhang,Song,Song –Can Chen,Artificial intelligence in medical Volume 32,Issue 1,page 37-50,September 2004.
  4. . Rafael C. Gonzalez, Richard E.Woods, “Digital Image Processing”, Pearson Education, Second Edition, ISBN 81-7758-168-6, 2005
  5. . Yingjie Wang, “Fuzzy Clustering Analysis Using Genetic Algorithm”, and ICIC International @ 2008 ISSN 1881-803 X, pp: 331—337.
  6. M. Sonka V Hlavac and R.Boyie ,”Image processing,analsis and machine version”,Third edition,Thomson,USA,2008 Iv:208-31
  7. S. Krinidis and V. Chatzis, J. IEEE Transactions on Image Processing, vol. 19, no. 5, (2010)
  8. V. S. Rao and Dr. S. Vidyavathi, “Comparative Investigations and Performance Analysis of FCM and MFPCM Algorithms on Iris data”,Indian Journal of Computer Science and Engineering, vol.1, no.2, 2010 pp. 145-151.
  9. SIKKA, K.—SINHA, N.—SINGH, P. K.—MISHRA, A. K.: A Fully Automated Algo-rithm Under Modified FCM Framework for Improved Brain MR Image Segmentation. Magnetic Resonance Imaging, Vol. 27, 2009, No. 7, pp. 994–1004.10] K.Srinivas, P.V.S.Srinivas, A.Govardhan, V.ValliKumari, “Periodic Web Personalization for Meta Search Engine”, IJCST, vol. 2, no. 4,December 2011.
  10. Davoud Sedighizadeh and Ellips Masehian, “Particle Swarm Optimization Methods, taxonomy and applications”, International Journal of Computer Theory and Engineering (1793-8201) Vol. 1, No. 5, December,2009, pp: 486-502 SheetalChouhan, Manish Shrivastava and KavitaDeshmukh, “A Noble Approach of Web Log Mining”, VSRD-IJCSIT, vol. 2, 2012
  11. Romesh Laishram, W.Kanan Kumar Singh, N.Ajit Kumar, Robindro.K, S.Jimriff, “MRI Brain Edge Detection Using GAFCM Segmentation and Canny Algorithm”, International Journal of Advances in Electronics Engineering – IJAEE,volume 2 - Issue 3, ISSN:- 2278-215X, pp. 168-171,December 8,2012.
  12. Gaussian smoothing." International Journal of Computational Intelligence in Bioinformatics and Systems Biology 1(3): 316-331. December 3, 2012
  13. Ortiz, A., J. Gorriz, et al. (2012). "Unsupervised Neural Techniques Applied to MR Brain Image Segmentation." Advances in Artificial Neural Systems 2012.
Index Terms

Computer Science
Information Sciences

Keywords

FCM Particle swarm optimization based FCM spatial information based FCM.