Circular/wrap-around self-organizing map networks: An empirical study in clustering and classification

Journal of the Operational Research Society

Volumn 52, Issue 1, 2001, Pages 93-101

  Kiang, M Y ^a   Kulkarni, U R ^b   St Louis, R D ^b  

^a CALIFORNIA STATE UNIVERSITY   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Cluster analysis; Group technology; Kohonen SOM networks; Neural networks

Indexed keywords

ALGORITHMS; COMPUTER ARCHITECTURE; GROUP TECHNOLOGY; SELF ORGANIZING MAPS;

SELF ORGANIZING MAP (SOM) NETWORKS;

NEURAL NETWORKS;

EID: 0034817925     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2601049     Document Type: Article

References (31)

1. Kohonen T (1984). In; ER Caianiello and G Musso (eds). Cybernetic Systems: Recognition, Learning, Self-Organization Research Studies Press; Letchworth, Herts, UK, p 3.
2. Kohonen T (1989). Self-Organization and Associative Memory. Springer-Verlag; Berlin, 3rd edn.
3. Kohonen T (1993). Physiological interpretation of the selforganizing map algorithm. Neural Networks 6: 895-905.
4. Kohonen T (1995). Self-Organizing Maps, Springer-Verlag: Berlin.
5. Leinonen L, Hiltunen T, Torkkola K and Kangas J (1993). Selforganized acoustic feature map in detection of locative-vowel coarticulation. JAcoust Soc Am 93: 3468-3474.
6. Manikopoulos CN (1993). Finite state vector quantisation with neural network classification of states. IEEE Proc F 140: 153-161.
7. Zhang B, Fu M and Yan H (1999). Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM). IEEE Trans Neural Networks 10: 939-945.
8. Chi Z and Yan H (1995). Handwritten numeral recognition using a small number of fuzzy rules with optimized defuzzification parameters. Neural Networks 8: 821-827.
9. Wurtz RP, Konen W and Behrmann KO (1999). On the performance of neuronal matching algorithms. Neural Networks2: 127-134.
10. Di NC, Macagnano A and D’Amico A (1997). Electronic-nose modelling and data analysis using a self-organizing map. Meas Sci Technol 8: 1236-1243.
11. Kohonen T, Oja E and Simula O (1996). Engineering applications of the self-organizing map. Proc IEEE 84: 1358-1384.
12. Vercauteren L, Sieben G, Praet M, Otte G, Vingerhoeds R, Boullart L, Calliauw, L and Roles H (1990). The classification of brain tumours by a topological map. Proceedings of the International Neural Networks Conference. Paris, pp 387-391.
13. Andreu G, Crespo A and Valiente JM (1997). Selecting the toroidal self-organizing feature maps (TSOFM) best organized to object recognition. Proceedings of the IEEE International Conference on Neural Networks. IEEE Computer Society Press, Vol. 21, pp 1341-1346.
14. Kiang MY, Kulkarni U, Goul MK, Philippakis A, Chi RT and Turban E (1997). Improving the converging property of the selforganizing map network-using a circular wrap-around Kohonen layer. Proceedings of the 30th Annual Hawaii International Conference on System Sciences, Sprague RH Jr (ed). IEEE Computer Science Press 5: 521-523.
15. Cottrell M and Fort JC (1986). A stochastic model of retinotopy: a self-organizing process. Biol Cybern 53: 405-411.
16. Ritter H and Schulten K (1986). On the stationary state of Kohonen’s self-organizing sensory mapping. Biol Cybern 54: 99-106.
17. Lo ZP and Bavarian B (1991). On the rate of convergence in topology preserving neural networks. Biol Cybern 65: 55-63.
18. Mitra S and Pal SK (1994). Self-organizing neural network as a fuzzy classifier. IEEE Trans Syst Man Cybern 24: 385-399.
19. Kohonen T (1991). Self-organizing maps: optimization approaches. In: Kohonen T, Makisara K, Simula O, and Kangas (eds). Artificial Neural Networks. North-Holland: Amsterdam, pp 981-990.
20. Ritter H and Schulten K (1988). Kohonen’s self-organizing maps: exploring their computational capabilities. Proceedings of the IEEE International Conference on Neural Networks. IEEE Computer Science Press, pp 1109-1116.
21. DeSieno D (1988). Adding a conscience to competitive learning. Proceedings of the International Conference on Neural Networks. IEEE Press: New York, Vol. 1, pp 117-124.
22. Lo ZP, Yu Y and Bavarian B (1993). Analysis of the convergence properties of topology preserving neural networks. IEEE Trans Neural Networks 4: 207-220.
23. Burbridge JL (1971). Production flow analysis. Engn April/ May: 138-152.
24. Kiang MY, Kulkarni U and KY Tam (1995). Self-organizing map network as an interactive clustering tool-an application to Group Technology. Decis Support Syst 15: 351-374.
25. Chan HM and Milner DA (1982). Direct clustering algorithm for group formation in cellular manufacturing. J Manufac Syst1: 65-75.
26. King JR (1980). Machine-component grouping in production flow analysis: An approach using a rank order clustering algorithm. Int J Prod Res 18: 213-232.
27. King JR and Nakornchai V (1982). Machine-component grouping formation in group technology: review and extension. Int J Prod Res 20: 117-133.
28. Wu HL, Venugopal, R and Malsburg MM (1986). Design of a cellular manufacturing system: a syntactic pattern recognition approach. JManufact Sys 5: 81-88.
29. Kumar CS and Chandrasekharan MP (1990). Grouping efficacy: a quantitative criteria for goodness of block diagonal forms of binary matrices in group technology. Int J Prod Res 28: 233-243.
30. Kulkarni UR and Kiang MY (1995). Dynamic grouping in parts in flexible manufacturing system-a self-organizing neural networks approach. Eur J Oper Res 84: 192-212.
31. Kirk RE (1968). Experimental Design: Procedures for the Behavioral Science. Brooks Cole Publishing Company: Belmont, pp 27-33.