Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31340
Knowledge Representation Based On Interval Type-2 CFCM Clustering

Authors: Myung-Won Lee, Keun-Chang Kwak


This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1938


[1] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate resoning-1”, Information Sciences, Vol. 8, pp.199-249, 1971.
[2] N. N. Karnik, J. M. Mendel, An Introduction to Type-2 Fuzzy Logic Systems, Univ. of Southern California, Los Angeles, CA, June, 1998.
[3] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems, Prentice Hall, 2001.
[4] C. Hwang, F. C. H. Rhee, “Uncertain fuzzy clustering: Interval type-2 fuzzy approach to c-means”, IEEE Trans. on Fuzzy Systems, Vol. 15, No. 1, pp. 107-120, 2007.
[5] O. Linda, M. Manic, “General type-2 fuzzy c-means algorithm for uncertain fuzzy clustering”, IEEE Trans. on Fuzzy Systems, Vol. 20, No. 5, pp. 883-897, 2012.
[6] C. Qiu, J. Xiao, L. Han, M. N. Iqbal, “Enhanced interval type-2 fuzzy c-means algorithm with improved initial center”, Pattern Recognition Letter, Vol. 38, pp .86-92, 2014.
[7] N. N. Karnik, J. M. Mendel, “Centroid of a type-2 fuzzy set”, Information Sciences, Vol. 132, No. 1, pp.195-220, 2001.
[8] L. Yao, K. S. Weng, “On a type-2 fuzzy clustering algorithm”, The Fourth International Conference on Advanced Cognitive Technologies and Applications, pp. 45-50, 2012.
[9] W. Pedrycz, “Conditional fuzzy c-means”, Pattern Recognition Letter, Vol.17, pp.625-632, 1996.
[10] W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks”, IEEE Trans. on Neural Networks, Vol. 9, No. 4, pp. 601-612, 1998.
[11] W. Pedrycz and A. V. Vasilakos, “Linguistic models and linguistic modeling”, IEEE Trans. on Systems, Man, and Cybernetics-Part C, Vol. 29, No.6, pp.745-757, 1999.
[12] K. C. Kwak, D. H. Kim, “Adaptive neuro-fuzzy networks with the aid of fuzzy granulation”, IEICE Trans. Information & Systems, Vol. E88D, No. 9, pp. 2189-2196, 2005.
[13] A. Tsanas, A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools”, Energy and Buildings, Vol. 49, pp. 560-567, 2012.