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Data Mining using Modified GFMM Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 116 - Number 15 |
| Year of Publication: 2015 |
| Authors: Supriya U. Kulkarni, Balaji S. Shetty |
10.5120/20411-2786
|
Supriya U. Kulkarni, Balaji S. Shetty . Data Mining using Modified GFMM Neural Network. International Journal of Computer Applications. 116, 15 ( April 2015), 18-22. DOI=10.5120/20411-2786
Abstract
The fuzzy neural networks are adaptive, learns quickly and are highly suitable in decision making where uncertainty is involved. In this paper the Modified General Fuzzy Min-Max Neural Network (MGFMMNN) is described which is experimented for the data mining tasks such as classification and clustering. The MGFMMNN utilizes fuzzy sets as pattern classes in which each fuzzy set is a union of fuzzy set hyperboxes. It is an extension of the general fuzzy min-max (GFMM) neural network proposed by Gabrys and Bargiala. The data mining tasks such as classification and clustering have been studied using MGFMMNN and Fisher Iris data set. Further, MGFMMNN is trained using Hepatitis Data Set to verify its classification and recognition ability. The results obtained are awfully persuading and confirms the effectiveness of the proposed system. The technique proposed is quick and reliably deployable in the applications that need classification and clustering.
References
- J. M. Zurada, Introduction to Artificial Neural Systems, Bombay: Jaico Publishing House, 1994.
- Simpson, P. K. 1992. Fuzzy min-max neural networks – Part 1: Classification. IEEE Trans. on Neural Networks. Vol. 3, No. 5, 776-786.
- Simpson, P. K. 1993. Fuzzy min-max neural networks – Part 2: Clustering. IEEE Trans. on Fuzzy Systems. Vol. 1, No. 1, 32-45.
- Gabrys, B. and Bargiela, A. 2000. General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Networks. Vol. 11, No. 3, 769-783.
- Kulkarni, U. V. , Sontakke, T. R. , and Randale, G. D. 2001. Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition. In Proceedings of IEEE: INNS: IJCNN 2001 Joint International Conference on Neural Networks. Washington DC, USA. Vol. 4, 2918-2923.
- Kwan, H. K. and Cai, Y. 1994. A Fuzzy neural network and its application to pattern recognition. IEEE Transactions on Fuzzy Systems. Vol. 2, No. 3, 185-192.
- Kulkarni, U. V. , Sontakke, T. R. , and Kulkarni, A. B. 2001. Fuzzy hyperline segment clustering neural network. Electronics Letters. Vol. 37, No. 5, 301-303.
- Patil, P. M. , Kulkarni, U. V. , and Sontakke, T. R. 2002. General Fuzzy Hyperline Segment Neural Network. IEEE International Conference on Systems, Man and Cybernetics, Hammamet, Tunisia. Volume 4. 6.
- Nandedkar, A. V. , and Biswas, P. K. 2007. A fuzzy min-max neural network classifier with compensatory neuron architecture. IEEE Transactions on Neural Networks. Volume 18. 42-54.
- Reza Davtalab, Mir Hossein Dezfoulian, and Muharram Mansoorizadeh. 2014. Multi-level fuzzy min-max neural network classifier. IEEE Transactions on Neural Networks. Volume 25. 470-482.
- Zhang, H. , Liu, J. , Ma, D. and Wang, Z. 2011. Data-core-based fuzzy min-max neural network for pattern classification. IEEE Transactions on Neural Networks. Volume 22, 2339-2352.
- Bose, N. K. and Liang, P. 1998. Neural Network Fundamentals with Graphs, Algorithms, and Applications. New Delhi: Tata McGraw-Hill.
- UCI repository of machine learning databases. 1998. University of California, Irvine. http://www. ics. uci. edu/mlearn/MLRepository. html
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