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Reseach Article

An Intelligent Fuzzy Convex Hull based Clustering Approach

by Rita Keshari, Amit Sinha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 8
Year of Publication: 2014
Authors: Rita Keshari, Amit Sinha
10.5120/17549-8145

Rita Keshari, Amit Sinha . An Intelligent Fuzzy Convex Hull based Clustering Approach. International Journal of Computer Applications. 100, 8 ( August 2014), 38-41. DOI=10.5120/17549-8145

@article{ 10.5120/17549-8145,
author = { Rita Keshari, Amit Sinha },
title = { An Intelligent Fuzzy Convex Hull based Clustering Approach },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 8 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number8/17549-8145/ },
doi = { 10.5120/17549-8145 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:28.924583+05:30
%A Rita Keshari
%A Amit Sinha
%T An Intelligent Fuzzy Convex Hull based Clustering Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 8
%P 38-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining refers to the extraction of knowledge by analyzing the data from different perspectives and accumulates them to form an useful information which could help the decision makers to take appropriate decisions. Classification and clustering has been the two broad areas in data mining. As the classification is a supervised learning approach, the clustering is an unsupervised learning approach and hence can be performed without the supervision of the domain experts. The basic concept is to group the objects in such a way so that the similar objects are closer to each. In this paper, an approach is made by fusing the concept of convex hull with fuzziness parameter. Each boundary data point is validated for the convex hull property to form a cluster. The decision value depends on the membership value of the particular point. The points satisfying the convex hull property forms a cluster. The performance

References
  1. Ravichandran,I. 2003, Data mining and clustering techniques, Technical Report.
  2. Jain, A. and Dubes, R. 1988. Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, NJ.
  3. Ertoz, L. , Stienbach, M. , and Kumar, V. 2002. Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data, Technical Report.
  4. Ester, M. , Kriegel, H-P. , Sander, J. and XU, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd ACM SIGKDD, 226-231, Portland, Oregon.
  5. Guha, S. , Rastogi, R. , and Shim, K. 1998. CURE: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD Conference, 73-84, Seattle, WA.
  6. Han, J. and Kamber, M. 2001. Data Mining. Morgan Kaufmann Publishers.
  7. J. Wang, B. Yang , W. Zhang and B. Qin, "Convex hull-based support vector machine rule extraction", 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), page 689 – 692, 2012.
  8. A. A. Ramli, J. Watada and W. Pedrycz, "Real Time Model of Fuzzy Random Regression Based on a Convex Hull Approach", International Conference on Advances in Computing, Control, and Telecommunication Technologies, pages 45-49, 2010.
  9. S. Theodoridis and K. Koutroumbas, "Pattern Recognition", fourth edition,Elsevier, 2009.
  10. H. Liu, S. Xiong ; Q. Chen, "Fuzzy Support Vector Machines Based on Convex Hulls", IEEE International Symposium on Knowledge Acquisition and Modeling, pages 920-923, 2008.
  11. W. Pedeyez, J. V. De, and Oliveria, "Advances in Fuzzy Clustering and its Applications", John Wiley & Sons, New York, 2007.
  12. P. D´?az, D. R. Llanos, B. Palop, "Parallelizing 2D- Convex Hulls on clusters: Sorting matters", XV Journal on Parallelism, 2004.
  13. S. Nascimento, B. Mirkin, and F. Moura Pires, "Modeling proportional membership in fuzzy clustering", IEEE Transactions on Fuzzy Systems, 11(2):173–186, 2003.
  14. S. Nascimento, B. Mirkin, and F. Moura Pires, "A fuzzy clustering model of data fuzzy c- means", In the Ninth IEEE International Conference on Fuzzy Systems,volume 1, pages 302–307, 2000.
  15. C. B. Barber, D. P. Dobkin and H. HuhdanPaa, "The Quickhull Algorithm for ConvexHulls", ACM Transactions on Mathematical Software, 22 (4), 1996.
  16. B. B. Chaudhari, "Fuzzy convex hull determination in 2-D space", Pattern Recognition Letters, volume 12(10), pages 591-594, 1991.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Clustering Convexity Tangent Convex hull.