CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Refinement of K-Means and Fuzzy C-Means

by A. Banumathi, A. Pethalakshmi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 17
Year of Publication: 2012
Authors: A. Banumathi, A. Pethalakshmi
10.5120/4911-7441

A. Banumathi, A. Pethalakshmi . Refinement of K-Means and Fuzzy C-Means. International Journal of Computer Applications. 39, 17 ( February 2012), 11-16. DOI=10.5120/4911-7441

@article{ 10.5120/4911-7441,
author = { A. Banumathi, A. Pethalakshmi },
title = { Refinement of K-Means and Fuzzy C-Means },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 17 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number17/4911-7441/ },
doi = { 10.5120/4911-7441 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:39.410591+05:30
%A A. Banumathi
%A A. Pethalakshmi
%T Refinement of K-Means and Fuzzy C-Means
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 17
%P 11-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering is widely used technique in data mining application for discovering patterns in large data set. In this paper the K-Means and Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seeds where it is selected either sequentially or randomly. For real time large database it’s difficult to predict the number of cluster and initial seeds accurately. In order overcome this drawback we propose two new algorithms Unique Clustering through Affinity Measure(UCAM) and Fuzzy-UCAM clustering algorithm. Both UCAM and Fuzzy-UCAM clustering algorithms works without fixing initial seeds, number of resultant cluster to be obtained. Unique clustering is obtained with the help of affinity measures.

References
  1. Hajing Li,Zaiquing Nie, WangChien Lee.: Scalable community Discovery on Textual Data with relations [http://www.ics.uci.edu/~mlearn/ MLRepository .html] Irvine, CA: University of California, Department of Information and Computer Science.
  2. S. Guha, R. Rastogi, and K. Shim. CURE.: An efficient clustering algorithm for large databases. In Proc. 1998 ACM6SIGMOD Int. Conf. Management of Data (SIGMOD’98), pages 73–84, 1998.
  3. Chen Zhang and Shixiong Xia.: K-Means Clustering Algorithm with Improved Initial center, in Second International Workshop on Knowledge Discovery and Data Mining (WKDD), pp. 7906792, 2009.
  4. F. Yuan, Z. H. Meng, H. X. Zhangz, C. R. Don.: A New Algorithm to Get the Initial Centroids”, proceedings of the 3rd International Conference on Machine Learning and Cybernetics, pp. 26629, August 2004.
  5. Chaturvedi J. C. A, Green P, “K - Modes clustering.” Journals of Classification, (18):35–55, 2001.
  6. Doulaye Dembele and Philippe Kastner, “Fuzzy C means method for clustering microarray data”, bioinformatics, vol.19, no.8, pp.9736 980, 2003.
  7. Dongxiao Zhu, Alfred O Hero, Hong Cheng, Ritu Khanna and Anand Swaroop, “Network constrained clustering for gene microarray Data”, doi:10.1093 bioinformatics / bti 655, Vol. 21 no. 21, pp. 4014 – 4020, 2005.
  8. G.K. Gupta .: Data mining with case studies.
  9. Dougherty ER, Barrera J, Brun M, Kim S, Cesar RM, Chen Y, et al. Inference from clustering with application to gene-expression microarrays. J Comput Biol 2002;9(1):105–26.
  10. Gasch AP, Eisen MB. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol 2002;3(11) RESEARCH0059.
  11. Bezdek J. Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press; 1981.
  12. RM Suresh, K Dinakaran, P Valarmathie, “Model based modified k-means clustering for microarray data”, International Conference on Information Management and Engineering, Vol.13, pp 271-273, 2009, IEEE.
  13. Han, Kamber, “Datamining Concepts and Techniques”, Elsevier publications, 2005.
  14. Anil K. Jain and Richard C. Dubes, “Algorithms for clustering data”, Prentice Hall, New Jersey,1988.
  15. Anirban Mukhopadhyay, Ujjwal Maulik and Sanghamitra bandyopadhyay, “ Efficient two stage fuzzy clustering of microarray gene expression data”, International Conference on Information Technology(ICIT’06), 2006 IEEE.
  16. Shi Zhong, Joydeep Ghosh, “A unified framework for model based clustering”, Journal of Machine Learning Research 4 (2003) 1001-1037
  17. K.Dinakaran, RM.Suresh, P.Valarmathie, “Clustering gene expression data using self organizing maps, Journal of Computer Applications”, Vol.1, No.4, 2008.
  18. Han-Saem Park and Sung-Bae Cho, “Evolutionary fuzzy clustering for gene expression profile E. Diday, The symbolic approach in clustering, in: H.H. Bock (Ed.), Classi3cation and Related Methods of Data Analysis, North-Holland, msterdam, 1988.
  19. Y. El-Sonbaty, M.A. Ismail, Fuzzy clustering for symbolic data, IEEE Trans. Fuzzy Systems 6 (2) (1998) 195–204.
  20. K.C. Gowda, E. Diday, Symbolic clustering using a new dissimilarity measure, Pattern Recognition 24 (6) (1991) 567–578.
  21. K.C. Gowda, E. Diday, Symbolic clustering using a new similarity measure, IEEE Trans. System Man Cybernet. 22 (1992) 368–378Z.
  22. Huang andM. K. Ng, “A fuzzy k-modes algorithm for clustering categorical data,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 4, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Cluster K-Means UCAM Fuzzy C-Means Fuzzy-UCAM

Powered by PhDFocusTM