Enhanced Clustering Analysis and Visualization Using Kohonen's Self-Organizing Feature Map Networks
Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
[1] TR Circular, "Use of Artificial Neural Networks in Geomechanical and
Pavement Systems", Transportation Research Circular No. E-C012,
TRB, Washington, DC, 1999.
[2] K. Gopalakrishnan, M.R. Thompson, and A. Manik, "Rapid Finite-
Element Based Airport Pavement Moduli Solutions using Neural
Networks", Int J. Comp. Intelligence, 3 (1), 2006, pp. 63-71.
[3] S. Frias, J.E. Conde, M.A. Rodriguez, V. Dohnal, and J.P. Perez-
Trujillo, "Metallic content of wines from the Canary Islands (Spain).
Application of artificial neural networks to the data analysis",
Nahrung/Food, 46(5), 2002, pp. 370-375.
[4] SM.J. Benito, M.C. Ortiz, M. Sagrario, L. Sarabia, and M. Iniguez,
Analyst, 1999, 124, pp. 547-552.
[5] M.C. Garcia-Parrilla, G.A. Gonzalez, F.J. Heredia, and A.M. Troncoso,
J. Agric. Food Chem., 1997, 45, pp. 3487-3492.
[6] M.C. Garcia-Parrilla, F.J. Heredia, A.M. Troncoso, Food Res. Int., 1999,
32, pp. 433-440.
[7] M.C. Ortiz, A. Herrero, M.S. Sanchez, L. Sarabia, and M. Iniiguez,
Chemom. Intell. Lab. Syst., 1995, 28, pp. 273-285.
[8] S. Vlassides, J.G. Ferrier, D.E. Block, Biotechnol. Bioeng., 2001, 73, pp.
55-68.
[9] E. Marengo, M. Aceto, V.J. Maurino, Chromatogr. A, 2001, 94, pp.
123-137.
[10] L.X. Sun, K. Danzer, G. Thiel, J. Fresenius, Anal. Chem., 1997, 359, pp.
143-149.
[11] C.G. Raptis, C.I. Siettos, C.T. Kiranoudis, G.V. Bafas, J. Food Eng.,
2000, 46, pp. 267-275.
[12] T. Kohonen, "Self-Organized Formation of Topologically Correct
Feature Maps", Biological Cybernetics, Vol. 43, 1982, pp. 59-69.
[13] SDL Component Suite, "Kohonen Network - Background Information",
http://www.lohninger.com/helpcsuite/kohonen_network_-
_background_information.htm. Accessed online March 13, 2007.
[14] T. Kohonen, Self Organization and Associative Memory, Springer
Verlag, Berlin, 1989.
[15] G. Deichsel and H.J. Trampisch, Clusteranalyse und
Diskriminanzanalyse, Gustav Fischer Verlag, Stuttgart, New York,
1985.
[16] A. Ultsch and C. Vetter, "Self-Organizing-Feature-Maps versus
Statistical Clustering Methods: A Benchmark", Research Report 0994,
FG Neuroinformatik & K├╝nstliche Intelligenz, University of Marburg,
Denmark, 1995.
[17] .A. Hartigan, Clustering Algorithms, Wiley and Sons, New York, 1975.
[18] H. Späth, Cluster Analysis Algorithms, Chichester, UK, 1980.
[19] M.R. Andernberg, Cluster Analysis for Applications, New York,
Academic Press, 1973.
[20] G.J. McLachlan and K.E. Basford, Mixture Models, New York: Marcel
Dekker, Inc., 1988.
[21] Murtagh F. and Hernández-Pajares M. (1995). The Kohonen Self-
Organizing Map Method: An Assesment. Journal of Classification, 12,
165-190.
[22] L. Leinonen, T. Hiltunen, K. Torkkola, and J. Kangas, "Self-organized
acoustic feature map in detection of fricative-vowel coarticulation", J.
Acoust. Soc. Am., 93 (6), 1993, pp. 3468-3474.
[23] C.N. Manikopoulos, "Finite state vector quantisation with neural
network classification of states", IEEE Proc.-F, 140 (3), 1993, pp. 153-
161.
[24] A.D. Bimbo, L. Landi, S. Santini, "Three-dimensional planar-faced
object classification with Kohonen maps", Opt. Eng., 32 (6), 1993, pp.
1222-1234.
[25] M. Sabourin, A. Mitiche, "Modeling and classi6cation of shape using a
Kohonen associative memory with selective multiresolution", Neural
Networks 6, 1993, pp. 275-283.
[26] J.A. Walter, K.J. Schulten, "Implementation of self-organizing neural
networks for visuo-motor control of an industrial robot", IEEE Trans.
Neural Networks 4 (1), 1993, pp. 86-95.
[27] H. Ritter, T. Martinetz, K. Schulten, "Topology-conserving maps for
learning visuo-motorcoordination", Neural Networks 2, 1989, pp. 159-
168.
[28] L. Vercauteren, G. Sieben, M. Praet, G. Otte, R. Vingerhoeds, L.
Boullart, L. Calliauw, H. Roels, "The classi6cation of brain tumours by a
topological map", in Proc. of the International Neural Networks
Conference, Paris, 1990, pp. 387-391.
[29] M. Y. Kiang (2001). "Extending the Kohonen self-organizing map
networks for clustering analysis", Computational Statistics & Data
Analysis, Vol. 38, pp. 161-180.
[30] UCI Machine Learning Repository, Wine recognition data,
ftp://ftp.ics.uci.edu/pub/machine-learning-databases/wine/, Apr 16th,
2005.
[31] M. Cottrell and J.C. Fort, "A stochastic model of retinotopy: a selforganizing
process", Biol. Cybern., 53, 1986, pp. 405-411.
[32] H. Ritter and K. Schulten, "On the stationary state of Kohonen-s selforganizing
sensory mapping", Biol. Cybern., 54, 1986, pp. 99-106.
[33] Z.-P. Lo and B. Bavarian, "On the rate of convergence in topology
preserving neural networks", Biol. Cybern., 65, 1991, pp. 55-63.
[34] S. Mitra and S.K. Pal, "Self-organizing neural network as a fuzzy
classifier", IEEE Trans. Systems, Man, Cybernetics, 24 (3), 1994, pp.
385-399.
[35] D. DeSieno, "Adding a conscience to competitive learning", in Proc. of
the International Conference on Neural Networks, Vol. I, IEEE Press,
1988, New York, pp. 117-124.
[36] D. Merkl and A. Rauber, "Uncovering the hierarchical structure of text
archives by using an unsupervised neural network with adaptive
architecture", PADKK, LNAI 1805, 2000, pp. 384-395.
[37] J. Vesanto and E. Alhoniemi, "Clustering of the self-organizing map",
IEEE Trans. Neural Networks, 11 (3), 2000, pp. 586-600.
[38] F. Murtagh, "Interpreting the Kohonen self-organizing feature map using
contiguity-constrained clustering", Pattern Recognition Lett., 16, 1995,
pp. 399-408.
[1] TR Circular, "Use of Artificial Neural Networks in Geomechanical and
Pavement Systems", Transportation Research Circular No. E-C012,
TRB, Washington, DC, 1999.
[2] K. Gopalakrishnan, M.R. Thompson, and A. Manik, "Rapid Finite-
Element Based Airport Pavement Moduli Solutions using Neural
Networks", Int J. Comp. Intelligence, 3 (1), 2006, pp. 63-71.
[3] S. Frias, J.E. Conde, M.A. Rodriguez, V. Dohnal, and J.P. Perez-
Trujillo, "Metallic content of wines from the Canary Islands (Spain).
Application of artificial neural networks to the data analysis",
Nahrung/Food, 46(5), 2002, pp. 370-375.
[4] SM.J. Benito, M.C. Ortiz, M. Sagrario, L. Sarabia, and M. Iniguez,
Analyst, 1999, 124, pp. 547-552.
[5] M.C. Garcia-Parrilla, G.A. Gonzalez, F.J. Heredia, and A.M. Troncoso,
J. Agric. Food Chem., 1997, 45, pp. 3487-3492.
[6] M.C. Garcia-Parrilla, F.J. Heredia, A.M. Troncoso, Food Res. Int., 1999,
32, pp. 433-440.
[7] M.C. Ortiz, A. Herrero, M.S. Sanchez, L. Sarabia, and M. Iniiguez,
Chemom. Intell. Lab. Syst., 1995, 28, pp. 273-285.
[8] S. Vlassides, J.G. Ferrier, D.E. Block, Biotechnol. Bioeng., 2001, 73, pp.
55-68.
[9] E. Marengo, M. Aceto, V.J. Maurino, Chromatogr. A, 2001, 94, pp.
123-137.
[10] L.X. Sun, K. Danzer, G. Thiel, J. Fresenius, Anal. Chem., 1997, 359, pp.
143-149.
[11] C.G. Raptis, C.I. Siettos, C.T. Kiranoudis, G.V. Bafas, J. Food Eng.,
2000, 46, pp. 267-275.
[12] T. Kohonen, "Self-Organized Formation of Topologically Correct
Feature Maps", Biological Cybernetics, Vol. 43, 1982, pp. 59-69.
[13] SDL Component Suite, "Kohonen Network - Background Information",
http://www.lohninger.com/helpcsuite/kohonen_network_-
_background_information.htm. Accessed online March 13, 2007.
[14] T. Kohonen, Self Organization and Associative Memory, Springer
Verlag, Berlin, 1989.
[15] G. Deichsel and H.J. Trampisch, Clusteranalyse und
Diskriminanzanalyse, Gustav Fischer Verlag, Stuttgart, New York,
1985.
[16] A. Ultsch and C. Vetter, "Self-Organizing-Feature-Maps versus
Statistical Clustering Methods: A Benchmark", Research Report 0994,
FG Neuroinformatik & K├╝nstliche Intelligenz, University of Marburg,
Denmark, 1995.
[17] .A. Hartigan, Clustering Algorithms, Wiley and Sons, New York, 1975.
[18] H. Späth, Cluster Analysis Algorithms, Chichester, UK, 1980.
[19] M.R. Andernberg, Cluster Analysis for Applications, New York,
Academic Press, 1973.
[20] G.J. McLachlan and K.E. Basford, Mixture Models, New York: Marcel
Dekker, Inc., 1988.
[21] Murtagh F. and Hernández-Pajares M. (1995). The Kohonen Self-
Organizing Map Method: An Assesment. Journal of Classification, 12,
165-190.
[22] L. Leinonen, T. Hiltunen, K. Torkkola, and J. Kangas, "Self-organized
acoustic feature map in detection of fricative-vowel coarticulation", J.
Acoust. Soc. Am., 93 (6), 1993, pp. 3468-3474.
[23] C.N. Manikopoulos, "Finite state vector quantisation with neural
network classification of states", IEEE Proc.-F, 140 (3), 1993, pp. 153-
161.
[24] A.D. Bimbo, L. Landi, S. Santini, "Three-dimensional planar-faced
object classification with Kohonen maps", Opt. Eng., 32 (6), 1993, pp.
1222-1234.
[25] M. Sabourin, A. Mitiche, "Modeling and classi6cation of shape using a
Kohonen associative memory with selective multiresolution", Neural
Networks 6, 1993, pp. 275-283.
[26] J.A. Walter, K.J. Schulten, "Implementation of self-organizing neural
networks for visuo-motor control of an industrial robot", IEEE Trans.
Neural Networks 4 (1), 1993, pp. 86-95.
[27] H. Ritter, T. Martinetz, K. Schulten, "Topology-conserving maps for
learning visuo-motorcoordination", Neural Networks 2, 1989, pp. 159-
168.
[28] L. Vercauteren, G. Sieben, M. Praet, G. Otte, R. Vingerhoeds, L.
Boullart, L. Calliauw, H. Roels, "The classi6cation of brain tumours by a
topological map", in Proc. of the International Neural Networks
Conference, Paris, 1990, pp. 387-391.
[29] M. Y. Kiang (2001). "Extending the Kohonen self-organizing map
networks for clustering analysis", Computational Statistics & Data
Analysis, Vol. 38, pp. 161-180.
[30] UCI Machine Learning Repository, Wine recognition data,
ftp://ftp.ics.uci.edu/pub/machine-learning-databases/wine/, Apr 16th,
2005.
[31] M. Cottrell and J.C. Fort, "A stochastic model of retinotopy: a selforganizing
process", Biol. Cybern., 53, 1986, pp. 405-411.
[32] H. Ritter and K. Schulten, "On the stationary state of Kohonen-s selforganizing
sensory mapping", Biol. Cybern., 54, 1986, pp. 99-106.
[33] Z.-P. Lo and B. Bavarian, "On the rate of convergence in topology
preserving neural networks", Biol. Cybern., 65, 1991, pp. 55-63.
[34] S. Mitra and S.K. Pal, "Self-organizing neural network as a fuzzy
classifier", IEEE Trans. Systems, Man, Cybernetics, 24 (3), 1994, pp.
385-399.
[35] D. DeSieno, "Adding a conscience to competitive learning", in Proc. of
the International Conference on Neural Networks, Vol. I, IEEE Press,
1988, New York, pp. 117-124.
[36] D. Merkl and A. Rauber, "Uncovering the hierarchical structure of text
archives by using an unsupervised neural network with adaptive
architecture", PADKK, LNAI 1805, 2000, pp. 384-395.
[37] J. Vesanto and E. Alhoniemi, "Clustering of the self-organizing map",
IEEE Trans. Neural Networks, 11 (3), 2000, pp. 586-600.
[38] F. Murtagh, "Interpreting the Kohonen self-organizing feature map using
contiguity-constrained clustering", Pattern Recognition Lett., 16, 1995,
pp. 399-408.
@article{"International Journal of Architectural, Civil and Construction Sciences:55090", author = "Kasthurirangan Gopalakrishnan and Siddhartha Khaitan and Anshu Manik", title = "Enhanced Clustering Analysis and Visualization Using Kohonen's Self-Organizing Feature Map Networks", abstract = "Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
", keywords = "Artificial neural networks, cluster analysis, Kohonen maps, wine recognition.", volume = "2", number = "6", pages = "121-8", }
