Research Article

Type-2 Projected Gustafson-Kessel Clustering Algorithm

by  Charu Puri, Naveen Kumar
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Issue 14
Published: Jun 2017
Authors: Charu Puri, Naveen Kumar
10.5120/ijca2017914445
PDF

Charu Puri, Naveen Kumar . Type-2 Projected Gustafson-Kessel Clustering Algorithm. International Journal of Computer Applications. 167, 14 (Jun 2017), 1-6. DOI=10.5120/ijca2017914445

                        @article{ 10.5120/ijca2017914445,
                        author  = { Charu Puri,Naveen Kumar },
                        title   = { Type-2 Projected Gustafson-Kessel Clustering Algorithm },
                        journal = { International Journal of Computer Applications },
                        year    = { 2017 },
                        volume  = { 167 },
                        number  = { 14 },
                        pages   = { 1-6 },
                        doi     = { 10.5120/ijca2017914445 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2017
                        %A Charu Puri
                        %A Naveen Kumar
                        %T Type-2 Projected Gustafson-Kessel Clustering Algorithm%T 
                        %J International Journal of Computer Applications
                        %V 167
                        %N 14
                        %P 1-6
                        %R 10.5120/ijca2017914445
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

We propose a type-2 based clustering algorithm to capture data points and attributes relationship embedded in fuzzy subspaces. It is a modification of Gustafson Kessel clustering algorithm through deployment of type-2 fuzzy sets for high dimensional data. The experimental results have shown that type-2 projected GK algorithm perform considerably better than the comparative techniques.

References
  • Hinneburg, C. Aggarwal, and D.A. Keim, What is the nearest neighbor in high dimensional spaces? In Proceedings 26th International Conference on Very Large Data Bases (VLDB-2000), Cairo, Egypt, September 2000, pp. 506-515, Morgan Kaufmann (2000).
  • K. Jain, M.N. Murthy, P.J. Flynn, Data clustering: a review, ACM Comput. Survey, vol. 31(3) pp. 264-323, 1999.
  • Wiswedel and M.R.Berthold. Fuzzy clustering in parallel universies In Proc.Conf. North American Fuzzy Information Processing Society(NAFIPS 2005), pp. 567-572, 2005.
  • Aggarwal, J. Wolf, P. Yu, C. Procopiuc, and J. Park. Fast algorithms for projected clustering In Proceedings of the 1999 ACM SIGMOD international conference on Management of data, pp. 61-72. ACM Press, 1999.
  • Bohm, K. Railing, H.-P. Kriegel, P. Kroger, Density Connected Clustering with Local Subspace Preferences, ICDM, Fourth IEEE International Conference, pp. 27 - 34, Nov. 2004.
  • Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications. New York: Academic, 1980.
  • Lin, M.S. Yang, A Similarity Measure between Type-2 Fuzzy Sets with Its Application to Clustering, Proc. of Int. Conf. on Fuzzy Systems and Knowledge Discovery, pp. 726-731, 2007.
  • C.H. Rhee and C. Hwang, A Type-2 Fuzzy-c-Means clustering algorithm, In Proceedings of IEEE FUZZ Conference, Melbourne, Australia, pp.1926- 1929, December 2001.
  • Hoppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis, and Image Recognition, John Wiley Sons (1999).
  • J. Klir, B.Yuan, Fuzzy sets and Fuzzy Logic:Theory and Applications, Prentice Hall, Upper Saddle River, NJ, 1995.
  • J. Klir and T. A. Folger, Fuzzy Sets, Uncertainty and Information. Englewood Clifs, NJ: Prentice Hall, 1988.
  • Gan, J. Wu, A Fuzzy Subspace Algorithm for Clustering High Dimensional Data, ADMA, 2006.
  • B. Mitchell, Pattern recognition using type-II fuzzy Sets,Information Sciences pp. 409-418, 2005.
  • Abonyi and Balazas Feil, Cluster Analysis for Data Mining and System Identification, Birkhauser. (1) J.C. Bezdek, Pattern recognition with Fuzzy Objective Function Algorithm, Plenum Press, New York, 1981.
  • Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006.
  • M. Mendel, Advances in type-2 fuzzy sets and systems, Information Sciences, pp. 84110, 2007.
  • M. Mendel, R.I. John, Type-2 fuzzy sets made simple, IEEE Transactions on Fuzzy Systems, pp. 117127, 2002.
  • N Karnik and J.M. Mendel, Introduction to Type-2 Fuzzy Logic Systems, In Proc. 7th Intl. Conf. on Fuzzy Systems FUZZ-IEEE’98, pp. 915-920, 1998.
  • Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is ”nearest neighbor” meaningful? In C. Beeri and P. Buneman, editors, Database Theory - ICDT ’99,7th International Conference, Jerusalem, Israel, January 10-12, 1999, Proceedings, volume 1540 of Lecture Notes in Computer Science, pp. 217-235, 1999.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Type-2 Subspace Clustering Gustafson Kessel

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