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Soft TDCT: A Fuzzy Approach towards Triangle Density based Clustering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 90 - Number 8 |
| Year of Publication: 2014 |
| Authors: Hrishav Bakul Barua, Sauravjyoti Sarmah, Mukul Biswas |
10.5120/15592-4255
|
Hrishav Bakul Barua, Sauravjyoti Sarmah, Mukul Biswas . Soft TDCT: A Fuzzy Approach towards Triangle Density based Clustering. International Journal of Computer Applications. 90, 8 ( March 2014), 7-14. DOI=10.5120/15592-4255
Abstract
Patterns and useful trends in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification and formation of clusters, or densely populated regions in a dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs. The objective of this paper is to present a fuzzy logical approach towards clustering to refine the results obtained from the previous approach; Triangle-density based clustering technique (TDCT) [1], which was proposed in an earlier research paper in 2012. We hence name this algorithm as Soft Triangle Density Based Clustering Technique (STDCT). This algorithm incorporates soft clustering and is capable of identifying embedded clusters of arbitrary shapes as well as multi-density clusters over large spatial datasets with precision. Experimental results are reported to establish the superiority of the technique in terms of cluster quality and complexity. Fig. 1 depicts the formation of clusters of similar data.
References
- Hrishav Bakul Barua, Dhiraj Kumar Das and Sauravjyoti Sarmah, "A Density Based Clustering Technique For Large Spatial Data Using Polygon Approach", TDCT, IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661 Volume 3, Issue 6 (July-Aug. 2012), PP 01-10.
- J. Han and M. Kamber, Data Mining: Concepts and Techniques. India: Morgan Kaufmann Publishers, 2004.
- M. Ester, H. P. Kriegel, J. Sander and X. Xu, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", in International Conference on Knowledge Discovery in Databases and Data Mining (KDD-96), Portland, Oregon, 1996, pp. 226-231.
- W. Wang, J. Yang, and R. R. Muntz, "STING: A Statistical Information Grid Approach to Spatial data Mining", in Proc. 23rd International Conference on Very Large Databases, (VLDB), Athens, Greece, Morgan Kaufmann Publishers, 1997, pp. 186 - 195.
- G. Sheikholeslami, S. Chatterjee and A. Zhang, "Wavecluster: A Multiresolution Clustering approach for very large spatial database", in SIGMOD'98, Seattle, 1998.
- R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan, "Automatic subspace clustering of high dimensional data for data mining applications", in SIGMOD Record ACM Special Interest Group on Management of Data, 1998, pp. 94–105.
- H. S. Nagesh, S. Goil and A. N. Choudhary, "A scalable parallel subspace clustering algorithm for massive data sets", in Proc. International Conference on Parallel Processing, 2000, pp. 477.
- L. Ertoz, M. Steinbach and V. Kumar, "Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data", in SIAM International Conference on Data Mining (SDM '03), 2003.
- G. Karypis, Han and V. Kumar, "CHAMELEON: A hierarchical clustering algorithm using dynamic modeling", IEEE Computer, 32(8), pp 68-75, 1999.
- Y. Zhao, S. Mei, X. Fan, S. Jun-de. 2003. Clustering Datasets Containing Clusters of Various Densities. Journal of Beijing University of Posts and Telecommunications, 26(2):42-47.
- H. S. Kim, S. Gao, Y. Xia, G. B. Kim and H. Y. Bae, "DGCL: An Efficient Density and Grid Based Clustering Algorithm for Large Spatial Database", Advances in Web-Age Information Management (WAIM'06), pp. 362-371, 2006.
- M. Ankerst, M. M. Breuing, H. P. Kriegel and J. Sander, "OPTICS: Ordering Points To Identify the Clustering Structure", in ACMSIGMOD, pp. 49-60, 1999.
- S. Roy and D. K. Bhattacharyya, "An Approach to Find Embedded Clusters Using Density Based Techniques", in Proc. ICDCIT, LNCS 3816, pp. 523-535, 2005.
- S. Sarmah, R. Das and D. K. Bhattacharyya, "Intrinsic Cluster Detection Using Adaptive Grids", in Proc. ADCOM'07, Guwahati, 2007.
- S. Sarmah, R. Das and D. K. Bhattacharyya, "A Distributed Algorithm for Intrinsic Cluster Detection over Large Spatial Data" A grid-density based clustering Technique (GDCT), World Academy of Science, Engineering and Technology 45, pp. 856-866, 2008.
- Rajib Mall ,"Software Engineering".
- Available: http//steve. hollasch. net /cgindex/math /barycentric. html
- James C. Bezdek, Robert Ehrlich and William Full, "FCM: The Fuzzy c-Means Clustering Algorithm", Computer & Geosciences, Vol. 10, No 2-3, pp. 191-203,1984,printed in USA.
- CHIH-TANG CHANG, JIM Z. C. LAI AND MU-DER JENG, "A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement", JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 27, 995-1009 (2011)
- Hrishav Bakul Barua and Sauravjyoti Sarmah. Article: An Extended Density based Clustering Algorithm for Large Spatial 3D Data using Polyhedron Approach. International Journal of Computer Applications 58(2):4-15, November 2012. Published by Foundation of Computer Science, New York, USA(ISBN: 973-93-80871-32-3),(ISSN:0975 – 8887)
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