Abstract
In this chapter, we apply the AFS theory to propose an elementary algorithm of fuzzy clustering. In the proposed approach, each cluster is interpreted by taking advantage of the semantics captured by the AFS logic. Within the framework of AFS theory, we develop new techniques of feature selection, concept categorization and characteristic description (i.e.,the characteristic description of an object or a group of objects using the fuzzy concepts) which are often encountered in tasks of machine learning and pattern recognition. The elementary fuzzy clustering algorithm is evolved to three more elaborate fuzzy clustering techniques by incorporating new techniques of feature selection, concept categorization and characteristic description. We show that they are simpler and produce more interpretable results when contrasted with some existing techniques. Several benchmark data and the evaluation data of 30 companies are considered to evaluate the effectiveness of the proposed AFS fuzzy clustering algorithms. We provide a detailed comparative analysis in which we compare the obtained results with those produced by some “conventional” methods such as FCM, k-means, and some newer algorithms including a two-level SOM-based clustering algorithm. The proposed algorithms can be applied to the data sets with mixed features such as sub-preference relations and even those including descriptions of human intuitive judgment. We show that the flexibility of the approach comes from the fact that the distance function and the class number need not be given beforehand. These two facets offers a far more higher flexible and contribute to a powerful framework for representing human knowledge and studying intelligent systems encountered in real world applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bezdek, J.C.: Fuzzy mathematics in pattern classification. Ph.D. Dissertation, Appl. Math. Cornell Univ., Ithaca, NY (1973)
Bezdek, J.C.: A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Machine Intell. 2(1), 1–8 (1980)
Blake, C.L., Men, C.J.: UCI Repository of machine learning databases, University of California, Imine, Dept. of Information and Computer Science (1998–2003), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Baraldi, A., Alpaydin, E.: Constructive feedforward ART clustering networks—Part I and II. IEEE Trans. Neural Netw. 13(3), 645–677 (2002)
Beliakov, G., King, M.: Density based fuzzy c-means clustering of non-convex patterns. European Journal of Operational Research 173, 717–728 (2006)
Ben-Hur, A., Horn, D., Siegelmann, H., Vapnik, V.: Support vector clustering. J. Mach. Learn. Res. 2, 125–137 (2001)
Blum, A., Langley, P.: Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1-2), 245–271 (1997)
Camastra, F., Verri, A.: A novel kernel method for clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 801–805 (2005)
Chen, Y.L., Hu, H.L.: An overlapping cluster algorithm to provide non-exhaustive clustering. European Journal of Operational Research 173, 762–780 (2006)
Cord, A., Ambroise, C., Cocquerez, J.: Feature selection in robust clustering based on Laplace mixture. Pattern Recognition Letters 27, 627–635 (2006)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet 3(3), 32–57 (1974)
Dubois, D., Prade, H.: The three semantics of fuzzy sets. Fuzzy Sets and Systems 90, 141–150 (1997)
Ding, R., Liu, X.D., Chen, Y.: The Fuzzy Clustering Algorithm Based on AFS Topology. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 89–98. Springer, Heidelberg (2006)
El-Sonbaty, Y., Ismail, M.A.: Fuzzy Clustering for Symbolic Data. IEEE Transactions on Fuzzy Systems 6(2), 195–204 (1998)
Eltoft, T., de Figueiredo, R.: A new neural network for cluster-detection-and-labeling. IEEE Trans. Neural Netw. 9(5), 1021–1035 (1998)
Fortuna, J., Capson, D.: Improved support vector classification using PCA and ICA feature space modification. Pattern Recognition 37, 1117–1129 (2004)
Graver, J.E., Watkins, M.E.: Combinatorics with Emphasis on the Theory of Graphs. Springer, New York (1977)
Gustafson, D.E., Kessel, W.: Fuzzy clustering with a fuzzy covariance matrix. In: Proc. IEEE Conf. Decision Control, San Diego, CA, pp. 761–766 (1979)
Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Netw. 11(3), 769–783 (2000)
Girolami, M.: Mercer kernel based clustering in feature space. IEEE Trans. Neural Netw. 13(3), 780–784 (2002)
Gath, I., Geva, A.B.: Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 773–781 (1989)
Graver, J.E., Watkins, M.E.: Combinatorics with Emphasis on the Theory of Graphs. Springer, New York (1977)
Gowda, K.C., Diday, E.: Symbolic clustering using a new dissimilarity measure. Pattern Recogn 24(6), 567–578 (1991)
Hathaway, R., Bezdek, J.: Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst., Man, Cybern. 31(5), 735–744 (2001)
Halmos, P.R.: Measure theory. Springer, New York (1974)
Jain, A., Zongker, D.: Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Trans. Pattern Analysis and Machine Intelligence 19(2), 153–157 (1997)
Jenssen, R., Erdogmus, D.: Information cut for clustering using a gradient descent approach. Pattern Recognition 40, 796–806 (2007)
Keller, J.M., Gray, M.R., Givens Jr., J.A.: A fuzzy K-nearest neighbors algorithm. IEEE Trans. Systems Man, Cybernet. SMC 15(4), 580–585 (1985)
Kiang, M.Y.: Extending the Kohonen self-organizing map networks for clustering analysis. Comput. Stat. Data Anal. 38, 161–180 (2001)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43(1), 59–69 (1982)
Kim, K.H.: Boolean matrix Theory and Applications. Marcel Dekker, New York (1982)
Kirby, M.: Data Analysis: An empirical approach to dimensionality reduction and the study of Pattern. Wiley, New York (2000)
Kohavi, R., John, G.: Wrappers for Feature Subset Selection. Artificial Intelligence 97(1-2), 273–324 (1997)
Koller, D., Sahami, M.: Toward Optimal Feature Selection. In: Proc. 13th Int’l Conf. Machine Learning, pp. 284–292 (1996)
Liu, X.D.: The Fuzzy Theory Based on AFS Algebras and AFS Structure. Journal of Mathematical Analysis and Applications 217, 459–478 (1998)
Liu, X.D.: The Topology on AFS Algebra and AFS Structure. Journal of Mathematical Analysis and Applications 217, 479–489 (1998)
Liu, X.D.: A new fuzzy model of pattern recognition and hitch diagnoses of complex systems. Fuzzy Sets and Systems 104, 289–297 (1999)
Liu, X.D.: A New Mathematical Axiomatic System of Fuzzy Sets and Systems. Journal of Fuzzy Mathematics 3, 559–560 (1995)
Liu, X.D.: The Fuzzy Sets and Systems Based on AFS Structure, EI Algebra and EII algebra. Fuzzy Sets and Systems 95, 179–188 (1998)
Liu, X.D.: Two Algebra Structures of AFS structure. Journal of Fuzzy Mathematics 3, 561–562 (1995)
Liu, X.D., Pedrycz, W., Zhang, Q.L.: Axiomatics Fuzzy sets logic. In: IEEE International Conference on Fuzzy Systems, vol. 1, pp. 55–60 (2003)
Liu, X.D., Pedrycz, W.: The Development of Fuzzy Decision Trees in the Framework of Axiomatic Fuzzy Set Logic. Applied Soft Computing 7, 325–342 (2007)
Liu, X.D., Wang, W., Chai, T.Y.: The Fuzzy Clustering Analysis Based on AFS Theory. IEEE Transactions on Systems, Man and Cybernetics Part B 35(5), 1013–1027 (2005)
Liu, X.D., Liu, W.Q.: Credit rating analysis with AFS fuzzy logic. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 1198–1204. Springer, Heidelberg (2005)
Liu, X.D., Chai, T.Y., Wang, W.: AFS Fuzzy Logic Systems and Its Applications to Model and Control. International Journal of Information and Systems Sciences 2(3), 285–305 (2006)
Liu, X.D.: The Development of AFS Theory Under Probability Theory. International Journal of Information and Systems Sciences 3(2), 326–348 (2007)
Liu, X.D., Pedrycz, W., Chai, T.Y., Song, M.L.: The Development of Fuzzy Rough Sets with the Use of Structures and Algebras of Axiomatic Fuzzy Sets. IEEE Transactions on Knowledge and Data Engineering 21(3), 443–462 (2009)
Liu, X.D., Zhu, K.J., Huang, H.Z.: The Representations of Fuzzy Concepts Based on the Fuzzy Matrix Theory and the AFS Theory. In: IEEE International Symposium on Intelligent Control, Texas, USA, October 5-8 (2003)
Liu, X.D.: The Structure of Fuzzy Matrices. Journal of Fuzzy Mathematics 2, 311–325 (1994)
Liu, X.D., Zhang, Q.L.: The Fuzzy Cognitive Maps Based on AFS Fuzzy Logic. Dynamics of Continuous, Discrete and Impulsive Systems 11(5-6), 787–796 (2004)
Liu, X.D., Chai, T.Y., Wang, W., Liu, W.Q.: Approaches to the Representations and Logic Operations for Fuzzy Concepts in the Framework of Axiomatic Fuzzy Set Theory I. Information Sciences 177, 1007–1026 (2007)
Liu, X.D., Chai, T.Y., Wang, W., Liu, W.Q.: Approaches to the Representations and Logic Operations for Fuzzy Concepts in the Framework of Axiomatic Fuzzy Set Theory II. Information Sciences 177, 1027–1045 (2007)
Liu, X.D., Ren, Y.: Novel Artificial Intelligent Techniques via AFS Theory: Feature Selection, Concept Categorization and Characteristic Description. Applied soft Computting (submitted)
Liu, X.D., Wang, X.C., Pedrycz, W., Chai, T.Y.: The Fuzzy Clustering via Axiomatic Fuzzy Set Theory. Pattern Recognition (submitted)
Liang, G.S., Chou, T.Y., Han, T.C.: Cluster Analysis Based on Fuzzy Equivalence Relation. European Journal of Operational Research 166, 160–171 (2005)
Lee, H., Park, K., Bien, Z.: Iterative fuzzy clustering algorithm with supervision to construct probabilistic fuzzy rule base from numerical data. IEEE Transactions on Fuzzy Systems 16, 263–277 (2008)
Li, Y., Dong, M., Hua, J.: Localized feature selection for clustering. Pattern Recognition Letters 29, 10–18 (2008)
Law, M., Figueiredo, M., Jain, A.K.: Simultaneous Feature Selection and Clustering Using Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 124–138 (2004)
Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.: Simultaneous Feature Selection and Clustering Using Mixture Models. IEEE transactions on pattern analysis and machine intelligence 26(9), 1154–1166 (2004)
Merz, C.J., Murphy, P.M.: UCI Repository for Machine Learning Data-Bases. Dept. of Information and Computer Science, University of California, Irvine, CA (1996), http://www.ics.uci.edu/~mlearn/MLRepository.html
Mollineda, R., Vidal, E.: A relative approach to hierarchical clustering, in Pattern Recognition and Applications. In: Torres, M., Sanfeliu, A. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 56, pp. 19–28. IOS Press, Amsterdam (2000)
Martinetz, T.E., Schulten, K.J.: Neural-Gas network for vector quantization and its application to time-series prediction. IEEE Trans. Neural Networks 4(4), 558–569 (1993)
Mitchell, T.M.: Machine Learning. China Machine Press (2003)
Marcelloni, F.: Feature selection based on a modified fuzzy C-means algorithm with supervision. Information Sciences 151, 201–226 (2003)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 14, 849–856 (2001)
Pedrycz, W.: Statistically grounded logic operators in fuzzy sets. European Journal of Operational Research (2008) doi:10.1016/j.ejor.2007.12.009
Pal, N., Bezdek, J., Tsao, E.: Generalized clustering networks and Kohonen’s self-organizing scheme. IEEE Trans. Neural Netw. 4(4), 549–557 (1993)
Roberts, S.J., Everson, R., Rezek, I.: Maximum certainty data partitioning. Pattern Recognition 33, 833–839 (2000)
Ren, Y., Song, M.L., Liu, X.D.: New approches to the fuzzy clustering via AFS theory. International Journal of Information and Systems Science 3(2), 307–325 (2007)
Ren, Y., Wang, X.C., Liu, X.D.: Fuzzy clustering approaches based on AFS fuzzy logic I. In: Proceedings of the 6th World Congress on Control and Automation, Dalian, China, June 21 - 23 (2006)
Ren, Y.: Feature Selection, Concept Categorization and Characteristic Description Based on AFS Theory, Master Degree Thesis, Dalian Maritime university (2006)
Spath, H.: Cluster Dissection and Analysis: Theory FORTRAN Programs, Examples, translated by Goldschmidt, J., p. 226. Halsted Press, New York (1985)
Wu, K.L., Yang, M.S.: Alternative c-means clustering algorithms. Pattern Recognition 35, 2267–2278 (2002)
Wang, G.J.: Theory of topological molecular lattices. Fuzzy Sets and Systems 47, 351–376 (1992)
Wang, X.C.: The Fuzzy Clustering via Axiomatic Fuzzy Set Theory, Master Degree Thesis, Dalian Maritime university (2006)
Wang, L.D., Liu, X.D.: Concept analysis via rough set and AFS algebra. Information Sciences 178, 4125–4137 (2008)
Wang, G.j.: Theory of topological molecular lattices. Fuzzy Sets Systems 47, 351–376 (1992)
Wu, S.T., Chow, T.W.S.: Clustering of the Self-organizing Map Using a Clustering Validity Index Based on Inter-cluster and Intra-cluster Density. Pattern Recognition 37, 175–188 (2004)
Yang, X., Song, Q., Wu, Y.: A robust deterministic annealing algorithm for data clustering. Data & Knowledge Engineering 62, 84–100 (2007)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
Zhang, L.S., Liu, X.D.: Concept lattice and AFS algebra. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 290–299. Springer, Heidelberg (2006)
Zhang, Y.J., Liang, D.Q., Tong, S.C.: On AFS algebra part I. Information Sciences 167, 263–286 (2004)
Zhang, Y.J., Liang, D.Q., Tong, S.C.: On AFS algebra part II. Information Sciences 167, 287–303 (2004)
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Liu, X., Pedrycz, W. (2009). AFS Fuzzy Clustering Analysis. In: Axiomatic Fuzzy Set Theory and Its Applications. Studies in Fuzziness and Soft Computing, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00402-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-00402-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00401-8
Online ISBN: 978-3-642-00402-5
eBook Packages: EngineeringEngineering (R0)