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Community Ecology

, Volume 11, Issue 1, pp 120–126 | Cite as

A comparison of Self-Organizing Feature Map clustering with TWINSPAN and fuzzy C-means clustering in the analysis of woodland communities in the Guancen Mts, China

  • J. T. ZhangEmail author
  • S. Li
  • M. Li
Article

Abstract

SOFM (self-organizing feature map) clustering is powerful in analyzing and solving complicated and non-linear problems. This method was used and compared with fuzzy C-means clustering and TWINSPAN, the most common classification methods, in analysis of plant communities in the Guancen Mts., China. The dataset consisted of importance values of 112 species in 53 quadrats of 10 m × 20 m. All the three methods classified the 53 quadrats into eight groups, representing eight associations of vegetation. They were all effective in the analysis of ecological data. The consistency of SOFM clustering with fuzzy C-means clustering (FCM) and TWINSPAN classification was 81.1% and 94.3%, respectively. SOFM clustering has some advantages and more potentiality in application to studies of ecology.

Keywords

Artificial neural network Classification Plant community Quantitative method Vegetation-environment relation 

Abbreviations

ANN

Artificial Neural Network

SOFM

Self-Organizing Feature Map Clustering

TWINSPAN

Two-Way Indicator Species Analysis

FCM

Fuzzy C-Means clustering

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References

  1. Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.Google Scholar
  2. Boyce, R. L. and P. C. Ellison. 2001. Choosing the best similarity index when performing fuzzy set ordination on binary data. J. Veg. Sci. 12: 711–720.Google Scholar
  3. Chon, T.-S., S. P. Young, M. Kyong and E. Y. Cha. 1996. Patterniz-ing communities by using an artificial neural network. Ecol. Model. 90: 69–78.Google Scholar
  4. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psych. Meas. 20: 37–46.Google Scholar
  5. Ehsani, A. H. and F. Quiel. 2008. Geomorphometric feature analysis using morphometric parameterization and artificial neural networks. Geomorphology 99: 1–12.Google Scholar
  6. Ekosse, G. I. E. and K. S. Mwitondi. 2009. Self-orgnizing feature map (SOFM) algorithms applied to manganese mineralization in soils close to an abandoned manganese oxide mine. Fresenius Environ. Bull. 18: 2234–2242.Google Scholar
  7. Foody, G. M. 1999. Applications of the self-organising feature map neural network in community data analysis. Ecol. Model. 120: 97–107.Google Scholar
  8. Forti, A. and G. L. Foresti. 2006. Growing Hierarchical Tree SOM: An unsupervised neural network with dynamic topology. Neural Netw. 19 (10): 1568–1580.PubMedGoogle Scholar
  9. Gauch, H. G. 1982. Multivariate Analysis in Community Ecology. Cambridge University Press, London.Google Scholar
  10. Gevrey, M., S. Worner and N. Kasabo. 2006. Estimating risk of events using SOM models: A case study on invasive species establishment. Ecol. Model. 197: 361–372.Google Scholar
  11. Giraudel, J.L. and S. Lek. 2001. A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol. Model. 146: 329–339.Google Scholar
  12. Goodacre, R., M. J. Neal and D. B. Kell. 1994. Rapid identification using prolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs. J. Appl. Bacte-riol. 76: 124–134.Google Scholar
  13. Greig-Smith, P. 1983. Quantitative Plant Ecology (3rd ed.). Black-well Scientific Publications, Oxford.Google Scholar
  14. Hill, M. O. 1979. TWINSPN-A Fortran program for arranging mul-tivariate data in an ordered two-way table by classification of the individuals and atributes. Ithaca, Cornell University.Google Scholar
  15. Jongman, R. H., C. J. F. ter Braak and O. F. R. van Tongeren. 1995. Data Analysis in Community and Landscape Ecology. Pudoc, Wageningen.Google Scholar
  16. Kaufmann, A. 1975. Introduction to the theory of fuzzy subsets: Vol. 1: Fundamental Theoretical Elements. Academic Press, London.Google Scholar
  17. Kosiba, P. and A. Stankiewicz. 2007. Water trophicity of Utricularia microhabitats identified by meansof sofm as a tool in ecological modeling. Acta Soc. Bot. Pol. 76: 255–261.Google Scholar
  18. Lek, S., Y. S. Park, S. Ait-Mouloud and L. Deharveng. 2007. Collembolan communities in a peat bog versus surrounding forest analyzed by using self-organizing map. Ecol. Model. 203: 9–17.Google Scholar
  19. Liu, Z Y. (ed). 1992. Soils in Shanxi province. Science Press, Beijing. (in Chinese).Google Scholar
  20. Liu, T. W. and J. Y. Yue. 2004. Flora Shanxiensis. China Science and Technology Press, Beijing. (in Chinese).Google Scholar
  21. Ma, Z. Q. 2001. Vegetation of Shanxi Province. China Science and Technology Press, Beijing. (in Chinese).Google Scholar
  22. Makridis, M., N. Papamarkos and C. Chamzas. 2005. An innovative algorithm for solving jigsaw puzzles using geometrical and color features. Lect. Notes Comput. Sci. 3773: 966–976.Google Scholar
  23. Manomaisupat, P., B. Vrusias and K. Ahmad. 2006. Categorization of large text collections: Feature selection for training neural networks. Lect. Notes Comput. Sci. 4224: 1003–1013.Google Scholar
  24. Orloci, L. 1978. Multivariate Analysis in Vegetation Research (2nd ed.). The Hague. Junk.Google Scholar
  25. Pal, N. R., A. Laha and J. Das. 2005. Designing fuzzy rule based classifier using self-organizing feature map for analysis of mul-tispectral satellite images. Int. J. Remote Sens. 26: 2219–2240.Google Scholar
  26. Park, Y. S., T. S. Chon and I. S. Kwak. 2004. Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks. Sci. Total Environ. 327: 105–122.PubMedGoogle Scholar
  27. Penczak, T., A. Kruk and M. Grzybkowska. 2006. Patterning of impoundment impact on chironomid assemblages and their environment with use of the self-organizing map (SOM). Acta Oecologica - Int. J. Ecol. 30 (3): 312–321.Google Scholar
  28. Podani, J. 2000. Introduction to the Exploration of Multivariate Biological Data. Backhuys Publishers, Leiden.Google Scholar
  29. Rios, S. A., J. D. Velasquez and E. S. Vera. 2005. Using SOFM to improve web site text content. Lect. Notes Comput. Sci. 3611: 622–626.Google Scholar
  30. Rolecek, J., L. Tichy, D. Zeleny and M. Chytry. 2009. Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity. J. Veg. Sci. 20: 596–602.Google Scholar
  31. Salski, A. 2007. Fuzzy clustering of fuzzy ecological data. Ecol. Inform. 2: 262–269.Google Scholar
  32. Sarbu, C. and H. W. Zwanziger. 2001. Fuzzy classification and comparison of some Romanian and German mineral waters. Anal. Lett. 34: 1541–1552.Google Scholar
  33. Schalkoff, R., 1992. Pattern Recognition: Statistical Structural and Neural Approaches. Wiley, NY.Google Scholar
  34. Song, M. Y., Y. S. Park, I. S. Kwak, H. Woo and T. S. Chon. 2006. Characterization of benthic macroinvertebrate communities in a restored stream by using self-organizing map. Ecol. Inform. 1: 295–305.Google Scholar
  35. Stuart, N., T. Barratt and C. Place. 2006. Classifying the Neotropical savannas of Belize using remote sensing and ground survey. J. Biogeogr. 33 (3): 476–490.Google Scholar
  36. Ter Braak, C. J. F., H. Hoijtink, W. Akkermans and P. F. M. Verdonschot. 2003. Bayesian model-based cluster analysis for predicting macrofaunal communities. Ecol. Model. 160: 235–248.Google Scholar
  37. Wu, Z.Y. 1980. Vegetation of China. Science Press, Beijing, pp: 453–615. (In Chinese).Google Scholar
  38. Yuan, Z. R. 2000. The artificial neural network and its application. Qinghua University Press, Beijing. (in Chinese).Google Scholar
  39. Zhang, J.-T. 2005. Succession analysisof plant communities inaban-doned croplands in the Eastern Loess Plateau of China. J. Arid Environ. 63: 458–474.Google Scholar
  40. Zhang, J.-T., W. M. Ru and B. Li. 2006a. Relationships between vegetation and climate on the Loess Plateau in China. Folia Geobot. 41: 151–163.Google Scholar
  41. Zhang, J.-T. , Y. Xi and J. Li. 2006b. The relationships between environment and plant communities in the middle part of Taihang Mountain Range, North China. Community Ecol. 7: 155–163.Google Scholar
  42. Zhang, J.-T., Y. R. Dong and Y. X. Xi. 2008. A comparison of SOFM ordination with DCA and PCA in gradient analysis of plant communities in the midst of Taihang Mountains, China. Ecol. Inform. 3: 367–374.Google Scholar
  43. Zhang, L.J., C. M. Liu, C. J. Davis, D. S. Solomon, T. B. Brann and L. E. Caldwell. 2004. Fuzzy classification of ecological habitats from FIA data. Forest Sci. 50: 117–127.Google Scholar

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© Akadémiai Kiadó, Budapest 2010

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.College of Life SciencesBeijing Normal UniversityBeijingChina
  2. 2.Institute of Loess PlateauShanxi UniversityTaiyuanChina

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