Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm

Authors

  • Kulkarni AB CSE Department, SGGSIE&T, SRTM University, Nanded, India
  • Bonde SV EXTC Department, SGGSIE&T, SRTM University, Nanded, India
  • Kulkarni UV CSE Department, SGGSIE&T, SRTM University, Nanded, India

DOI:

https://doi.org/10.26438/ijcse/v6i5.9499

Keywords:

Fuzzy clustering, Fuzzy membership function, Fuzzy hyperspheres, pruning

Abstract

The paper proposes a new supervised fuzzy clustering algorithm based on inter-class and intra-class clustering technique to create the clusters i.e. fuzzy hyperspheres (FHSs) and pruning technique to prune redundant FHSs which are camouflaged by the other FHSs of the same class. The proposed clustering technique finds the centroid and the width of the FHS based on the spread of inter-class patterns and then groups intra-class patterns using fuzzy membership function, whereas the pruning technique creates the optimal number of FHSs from the FHSs created in the earlier stage. This algorithm is independent of parameters, limits the interference of outliers and converges quickly to create an optimal number of clusters. The main feature of the proposed fuzzy clustering algorithm is that it camouflages the clustered patterns giving 100% accuracy for any training dataset. The performance of the proposed algorithm is tested on eleven benchmark datasets and it is observed that the proposed algorithm results are superior and comparable with classifiers using clustering algorithm.

References

K. Rose, F. Guerewitz, G. Fox, “A Deterministic annealing Approach to Clustering”, Pattern Recognition Let., Vol. 11, Issue. 9, pp. 589-594, 1990.

L. Bai, J. Liang, C. Dang, F. Cao, “A Novel Fuzzy Clustering Algorithm With Between-Cluster Information for Categorical Data”, Fuzzy Sets Syst., Vol. 215, pp. 55-73, 2013.

F. Tung, A. Wong, D. A. Clausi, “Enabling Scalable Spectral Clustering for Image Segmentation”, Pattern Recogn., Vol 43, Issue. 12, pp. 4069-4076, 2013.

Y. Yan, L. Chen, W. C. Tjhi, “Fuzzy Semi-Supervised Co-Clustering for Text Documents”, Fuzzy Sets Syst., Vol 215, pp. 74-89, 2013.

B. Sun, W. Liu, Q. Zhong, “Hierarchical Speaker Identification Using Speaker Clustering,” Int. Conf. on Natural Language Processing and Knowledge Engineering, pp. 299-304 2003.

B. Dogan, M. Korurek, “A New Ecg Beat Clustering Method Based On Kernelized Fuzzy C-Means And Hybrid Ant Colony Optimization for Continuous Domains”, Appl. Soft Comput.,Vol 12 , Issue. 11, pp. 3442–3451, 2012.

Y. Chen, J. Wang, And R. Krovetz, “Clue: Cluster-Based Retrieval Of Images By Unsupervised Learning”, IEEE Trans. on Image Processing, Vol.14, Issue. 8, pp. 1187–1201, 2005.

C. R. Lin And M. Gerla, “Adaptive Clustering for Mobile Wireless Networks”, Journal on Selected Areas in Communication, Vol. 15, Issue. 7, pp.1265-1275, 1997.

R.N. Dave, R. Krishnpuram, “Robust Clustering Method: A Unified View”, IEEE Trans. Fuzzy System, Vol. 5, Issue. 2, pp. 270-293, 1997.

J. C. Bezdek, “Pattern Recognition With Fuzzy Objective Function Algorithms”, Plenum press, New York, 1981.

L. Kaufman, P.J. Rousseeuw, “Finding Groups In Data: An Introduction to Cluster Analysis”, Wiley, Hoboken, 2005.

N. R. Pal, K. Pal, J. M. Keller, J. C. Bezdek, “A Possibilistic Fuzzy C-Means Clustering Algorithm”, IEEE Trans. Fuzz,Y Syst., Vol.13, No.4, pp. 508-516, 2005.

Simpson P. K.,“Fuzzy Min-Max Neural Networks Part-2: Clustering”, IEEE Trans. Fuzzy System, Vol. 1, Issue. 1, pp.32-45, 1993.

U. V. Kulkarni, T. R. Sontakke, A. B. Kulkarni, “Fuzzy Hyperline Segment Clustering Neural Network”, Electronics Letters, Vol.37, Issue. 5, pp. 301-303, 2001.

J. C. Bezdek, N. R. Pal, “Generalized Clustering Networks And Kohonen’s Self-Organizing Scheme”, IEEE Neural Networks, Vol. 4, Issue. 4, pp. 549-557, 1993.

G. Carpenter, S. Grossberg, N. Maukuzon, J. Reynolds, And D. B. Rosen, “Fuzzy Artmap: A Neural Network Architecture for Incremental Supervised Learning Of Analog Multidimensional Maps”, IEEE Trans. Neural Networks,Vol. 3, Issue. 5, pp. 698-713, 1992.

A. Likas, N. Vlassis, Verbeek, “The Global K-Means Clustering Algorithm”, Pattern Recog. Let., Vol. 36, pp. 451-461, 2003.

D. W. Kim, K. H. Lee, D. Lee, “Fuzzy Cluster Validation Index Based On Inter-Cluster Proximity,” Pattern Recognition Letters, Vol. 24, Issue. 15, pp. 2561-2574, 2003.

A. B. Kulkarni, S. V. Bonde, U. V. Kulkarni, “A Novel Fuzzy Clustering Algorithm for Radial Basis Function Neural Network”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, Issue. 4, pp.751-756, 2018.

M. Rouhani, D. S. Javan, “Two Fast And Accurate Heuristic Rbf Learning Rules for Data Classification”, Neural Networks, Vol.75, pp. 150-161, 2016.

Yuanshan Liu, He Huang, Ting Wen Huang B, Xusheng Qian,“An Improved Maximum Spread Algorithm With Application to Complex-Valued Rbf Neural Networks”, Neurocomputing, Vol. 216, pp. 261-267, 2016.

Downloads

Published

2025-11-13
CITATION
DOI: 10.26438/ijcse/v6i5.9499
Published: 2025-11-13

How to Cite

[1]
A. Kulkarni, S. Bonde, and U. Kulkarni, “Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm”, Int. J. Comp. Sci. Eng., vol. 6, no. 5, pp. 94–100, Nov. 2025.

Issue

Vol. 6 No. 5 (2018): IJCSE May Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.