Self-Organizing Maps applied to ecological sciences

Ecological Informatics

Volumn 6, Issue 1, 2011, Pages 50-61

  Chon, Tae Soo ^a  

^a Pusan National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DATA INTERPRETATION; DATA SET; ECOLOGICAL APPROACH; ECOLOGICAL MODELING; ENVIRONMENTAL FACTOR; LITERATURE REVIEW; MAP; NUMERICAL MODEL; VISUALIZATION;

EID: 79651475285     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2010.11.002     Document Type: Review

References (181)

1. Aguado D., Montoya T., Borras L., Seco A., Ferrer J. Using SOM and PCA for analysing and interpreting data from a P-removal SBR. Eng. Appl. Artif. Intel. 2008, 21:919-930.
2. Aguilera P.A., Frenich A.G., Torres J.A., Castro H., Vidal J.L.M., Canton M. Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality. Water Res. 2001, 35:4053-4062.
3. Allen J.I., Somefield P.J., Gilbert F.J. Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models. J. Mar. Syst. 2007, 64:3-14.
4. Angelovič P. Time series prediction using RSOM and local models. IIT. SRC 2005: Student research conference 2005, 27-34.
5. Bauer H.U., Pawelzik K.R. Quantifying the neighbourhood preservation of self-organizing feature maps. IEEE T. Neural Networ. 1992, 3:570-579.
6. Bauer H.U., Villmann T. Growing a hypercubical output space in a self-organizing feature map. IEEE Trans. Neural Networ. 1997, 8:218-226.
7. Bayram E., Santiago P., Harris R., Xiao Y.D., Clauset A.J., Schmitt J.D. Genetic algorithms and self-organizing maps: a powerful combination for modelling complex QSAR and QSPR problems. J. Comp. Aid. Mol. Des. 2004, 18:483-493.
8. Besaw L.E., Rizzo D.M., Kline M., Underwood K.L., Doris J., Morrissey L.A., Pelletier K. Stream classification using hierarchical artificial networks: a fluvial hazard management tool. J. Hydrol. 2009, 373:34-43.
9. Bezdek J.C., Pal N.R. Two soft relatives of learning vector quantization. Neural Networ. 1995, 8:729-743.
10. Bowden G.J., Nixon J.B., Dandy G.C., Maier H.R., Holmes M. Forecasting chlorine residuals in a water distribution system using a general regression neural network. Math. Comput. Model. 2005, 44:469-484.
11. Bowden G.J., Dany G.C., Maier H.R. Input determination for neural network models in water resources applications. Part 1-background and methodology. J. Hydrol. 2006, 301:75-92.
12. Brosse S., Giraudel J.L., Lek S. Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecol. Model. 2001, 146:159-166.
13. Cao J.D. New results concerning exponential stability and periodic solutions of delayed cellular neural networks. Phys. Lett. A 2003, 307:136-147.
14. Carpinteiro O.A.S., Leme R.C., Zambroni de Souza A.C., Pinheiro C.A.M., Moreira E.M. Long-term load forecasting via a hierarchical neural model with time integrators. Electr. Pow. Syst. Res. 2007, 77:371-378.
15. Céréghino R., Park Y.-S. Review of Self-Organizing Map (SOM) approach in water resources: analysis, modeling and application. Environ. Modell. Softw. 2008, 23:835-845.
16. Céréghino R., Park Y.-S., Compin A., Lek S. Predicting the species richness of aquatic insects in streams using a limited number of environmental variables. J. N. Am. Benthol. Soc. 2003, 22:442-456.
17. Chan W.S., Recknagel F., Cao H., Park H.-D. Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms. Water Res. 2007, 41:2247-2255.
18. Chang F.-J., Chang L.-C., Kao H.-S., Wu G.-R. Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. J. Hydrol. 2010, 384:118-129.
19. Chappell G., Taylor J.G. The temporal Kohønen map. Neural Netw. 1993, 6:441-445.
20. Chen S.K., Mangiameli P., West D. The comparative ability of self-organizing neural networks to define cluster structure. Omega Int. J. Manage. S. 1995, 23:271-279.
21. Choi K.-H., Kim J.-S., Kim Y.S., Yoo M.-A., Chon T.-S. Pattern detection of movement behaviors in genotype variation of Drosophila melanogaster by using self-organizing map. Ecol. Inform. 2006, 1:219-228.
22. Chon T.-S., Park Y.-S. Ecological informatics as an advanced interdisciplinary interpretation of ecosystems. Ecol. Inform. 2006, 1:213-217.
23. Chon T.-S., Park Y.-S., Moon K., Cha E. Patternizing communities by using an artificial neural network. Ecol. Model. 1996, 90:69-78.
24. Chon T.-S., Park Y.-S., Park J.H. Determining temporal pattern of community dynamics by using unsupervised learning algorithms. Ecol. Model. 2000, 132:151-166.
25. Chon T.-S., Park Y.-S., Park K.Y., Choi S.-Y., Kim K.T., Cho E.C. Implementation of computational methods to pattern recognition of movement behavior of Blattella germanica (Blattaria: Blattellidae) treated with Ca2+ signal inducing chemicals. Appl. Entomol. Zool. 2004, 39:79-96.
26. Compin A., Céréghino R. Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in southwestern France. Landscape Ecol. 2007, 22:1215-1225.
27. Compin A., Park Y.-S., Lek S., Céréghino R. Species spatial distribution and richness of stream insects in south-western France using artificial neural networks with potential use for biosurveillance. Modelling Community Structure in Freshwater Ecosystems 2005, 221-238. Berlin, Springer-Verlag. S. Lek, M. Scardi, P.F.M. Verdonschot, J.P. Descy, Y.S. Park (Eds.).
28. Coste, M., Boutry, S., Tison-Rosebery, J., Delmas, F., 2009. Improvements of the Biological Diatom Index (BDI): description and efficiency of the new version (BDI-2006). 9, 621-650.
29. Dai Q., Chen S. Integrating the improved CBP model with kernel SOM. Neurocomputing 2006, 69:2208-2216.
30. de Bodt E., Cottrell M., Letremy P., Verleysen M. On the use of self-organizing maps to accelerate vector quantization. Neurocomputing 2004, 56:187-203.
31. Dittenbach M., Rauber A., Merkl D. Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing 2002, 48:2002.
32. Dzemyda G. Visualization of a set of parameters characterized by their correlation matrix. Comput. Stat. Data Anal. 2001, 36:15-30.
33. Ferrán E.A., Ferrara P. Clustering proteins into families using artificial neural networks. Comput. Appl. Biosci. 1992, 8:39-44.
34. Flanagan J.A. Sufficient condition for self-organisation in the one dimensional SOM with a reduced width neighbourhood. Neurocomputing 1998, 21:51-60.
35. Foody G. Applications of the self-organising feature map neural network in community data analysis. Ecol. Model. 1999, 210:97-107.
36. Foody G., Dash J. Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains, USA. Ecol. Inform. 2007, 2:89-93.
37. Fort J.C. SOM's mathematics. Neural Netw. 2006, 19:812-816.
38. French M.N., Recknagel F. Modeling of algal blooms in freshwaters using artificial neural networks. Computer Techniques in Environmental Studies V. Environmental Systems II 1994, 87-94. Computational Machanics Publication, Southampton, Boston. P. Zanetti (Ed.).
39. Furukawa T. SOM of SOMs: self-organizing map which maps a group of self-organizing maps. Lect. Notes Comput. Sci. 2005, 3696:391-396.
40. Gevrey M., Rimet F., Park Y.-S., Giraudel J.L., Ector L., Lek S. Water quality assessment using diatom assemblages and advanced modelling techniques. Freshw. Biol. 2004, 49:208-220.
41. Gevrey M., Worner S., Kasabov N., Pitt J., Giraudel J.-L. Estimating risk of events using SOM models: a case study on invasive species establishment. Ecol. Model. 2006, 127:361-372.
42. Ghosh S., Patra S., Ghosh A. An unsupervised context-sensitive change detection technique based on modified self-organizing feature map neural network. In. J. Appoximate Reasoning 2009, 50:37-50.
43. Giraudel J.L., Aurelle D., Berrebi P., Lek S. Application of the self-organizing mapping and fuzzy clustering to microsatellite data: how to detect genetic structure in brown trout (Salmo trutta) populations. Artificial Neuronal Networks, Application to Ecology and Evolution 2000, 187-202. Springer-Verlag, Berlin. S. Lek, J.F. Guégan (Eds.).
44. Gonzalez A.I., Graña M., Anjou A.D., Albizuri F.X. A sensitivity analysis of the self organizing map as an adaptive one-pass non-stationary clustering algorithm: the case of color quantization of image sequences. Neural Process. LeETT. 1997, 6:77-89.
45. Grabec I. Self-organization of neurons described by the maximum-entropy principle. Biol. Cybern. 1990, 63:403-409.
46. Griebeler E., Seitz A. The use of Markovian metapopulation models: reducing the dimensionality of transition matrices by self-organizing Kohonen networks. Ecol. Model. 2006, 192:271-285.
47. Grossberg S. How does a brain build a cognitive code?. Psychol. Rev. 1980, 87:1-51.
48. Hakimi-Asiabar M., Ghodsypour S., Kerachian R. Multi-objective genetic local search algorithm using Kohonen's neural map. Comput. Ind. Eng. 2009, 56:1566-1576.
49. Hammer B., Micheli A., Sperduti A., Strickert M. Recursive self-organizing network models. Neural Netw. 2004, 17:1061-1085.
50. Haykin S. Neural Networks: A Comprehensive Foundation 1994, Macmillan, New York, 695pp.
51. Hellawell J.M. Biological Indicators of Freshwater Pollution and Environmental Management 1986, Elsevier, London, 546pp.
52. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. PNAS 1982, 9:2554.
53. Hsu K.-C., Li S.-T. Clustering spatial-temporal precipitation data using wavelet transform and self-organizing map neural network. Adv. Water Res. 2010, 33:190-200.
54. Hyun K., Song M.-Y., Kim S., Chon T.-S. Using an artificial neural network to patternize long-term fisheries data from South Korea. Aquat. Sci. 2005, 67:382-389.
55. Jabiol J., Corbara B., Dejean A., Cereghino R. Structure of aquatic insect communities in tank-bromeliads in a East-Amazonian rainforest in French Guiana. For. Ecol. Manage. 2009, 257:351-360.
56. Jeong K.-S., Hong D.-G., Byeon M.-S., Jeong J.-C., Kim H.-G., Kim D.-K., Joo G.-J. Stream modification patterns in a river basin: field survey and self-organizing map (SOM) application. Ecol. Inform. 2010, 5:293-303.
57. Ji C.W., Choi K.H., Lee S.H., Park Y.-S., Chon T.-S. Monitoring of movement behaviors of chironomid larvae after exposure to diazinon using fractal dimension and self-organizing map. Int. J. Ecodyn. 2007, 2:1-12.
58. Jin H., Shum W.H., Leung K.S., Wong M.L. Expanding self-organizing map for data visualization and cluster analysis. Inf. Sci. 2004, 163:157-173.
59. Jockusch S., Ritter H. Self-organizing maps: local competition and evolutionary optimization. Neural Netw. 1994, 7:1229-1239.
60. Joo G.J., Jeong K.S. Modelling community changes of cyanobacteria in a flow regulated river (the lower Nakdong River, S. Korea) by means of a Self-Organizing Map (SOM). Modelling Community Changes of Cyanobacteria in a Flow Regulated River (the Lower Nakdong River, S. Korea) by Means of a Self-Organizing Map (SOM) 2005, 273-287. Springer Verlag, Berlin. S. Lek, M. Scardi, P. Verdonschot, J.P. Descy, Y.S. Park (Eds.).
61. Jørgensen S.V. Exery and ecology. Ecol. Model. 1992, 63:185-214.
62. Kaipainen M., Ilmonen T. Period detection and representation by recurrent oscillatory self-organizing map. Neurocomputing 2003, 55:699-710.
63. Kalteh A.M., Hjorth P., Berndtsson R. Review of the self-organizing map (SOM) approach in water resources: analysis, modeling and application. Environ. Modell. Softw. 2008, 23:835-845.
64. Kangur K., Park Y.-S., Kangur A., Kangur P., Lek S. Patterning long-term changes of fish community in large shallow Lake Peipsi. Ecol. Model. 2007, 203:34-44.
65. Kaski S., Kangas J., Kohonen T. Bibliography of self-organizing map (SOM) papers: 1981-1997. Neural Comput. Surv. 1998, 1:102-350.
66. Kim B., Lee S.-E., Song M.-Y., Choi J.-H., Ahn S.-M., Lee K.-S., Cho E., Chon T.-S., Koh S.-C. Implementation of artificial neural networks (ANNs) to analysis of inter-taxa communities of benthic microorganisms and macroinvertebrates in a polluted stream. Sci. Total Environ. 2008, 390:262-274.
67. Kita E., Kan S., Fei Z. Investigation of self-organizing map for genetic algorithm. Adv. Eng. Softw. 2010, 41:148-153.
68. Kohonen T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 1982, 43:59-69.
69. Kohonen T. Analysis of a simple self-organizing process. Biol. Cybern. 1982, 44:135-140.
70. Kohonen T. Physiological interpretation of the self-organizing map algorithm. Neural Netw. Oxford. 1993, 6:895-895.
71. Kohonen T. Self-Organizing Maps 2001, Springer, Berlin, 501pp. 3rd ed.
72. Kohonen T., Kaski S., Lappalainen H. Self-organized formation of various invariantfeature filters in the adaptive-subspace SOM. Neural Comput. 1997, 9:1321-1344.
73. Kontunen-Soppela S., Parviainen J., Ruhanen H., Brosché M., Keinäen M., Thakur R.C., Kolehmainen M., Kangasjävi J., Oksanen E., Karnosky D.F., Vapaavuori E. Gene expression responses of paper birch (Betula papyrifera) to elevated CO2 and O3 during leaf maturation and senescence. Environ. Pollut. 2010, 158:959-968.
74. Koskela T., Varsta M., Heikkonen J., Kaski K. Time series prediction using recurrent SOM with local linear models. Int. J. Knowl. Based Intell. Eng. Syst. 1998, 2:60-68.
75. Kropp J. A neural network approach to the analysis of city systems. Appl. Geogr. 1998, 18:83-96.
76. Kwak I.-S., Song M.-Y., Park Y.-S., Chon T.-S. Patterning community changes in benthic macroinvertebrates in a polluted stream by using artificial neural networks. Modelling Community Changes of Cyanobacteria in a Flow Regulated River (the Lower Nakdong River, S. Korea) by Means of a Self-Organizing Map (SOM) 2005, 239-249. Springer Verlag, Berlin. S. Lek, M. Scardi, P. Verdonschot, J.P. Descy, Y.S. Park (Eds.).
77. Lakany H. Extracting a diagnostic gait signature. Pattern Recogn. 2008, 41:1627-1637.
78. Lampinen J., Oja E. Clustering properties of hierarchical self-organizing maps. J. Math. Imaging Vis. 1992, 2:261-272.
79. Lasne E., Lek S., Laffaille P. Patterns in fish assemblages in the Loire floodplain: the role of hydrological connectivity and implications for conservation. Biol. Conserv. 2007, 139:258-268.
80. Lee W.-S., Kwon Y.-S., Yoo J.-C., Song M.-Y., Chon T.-S. Multivariate analysis and self-organizing mapping applied to analysis of nest-site selection in Black-tailed Gulls. Ecol. Model. 2006, 193:602-614.
81. Lee J., Kwak I.-S., Lee E., Kim K.A. Classification of breeding bird communities along an urbanization gradient using an unsupervised artificial neural network. Ecol. Model. 2007, 203:62-71.
82. Lee W.-S., Kwon Y.-S., Park Y.-S., Chon T.-S., Yoo J.-C. Evaluation of environmental factors to predict breeding success of black-tailed gulls. Ecol. Inform. 2007, 1:331-339.
83. Lee C.-W., Jang J., Jeong K., Kim D., Joo G. Patterning habitat preference of avifaunal assemblage on the Nakdong River estuary (South Korea) using self-organizing map. Ecol. Inform. 2009, 5:89-96.
84. Lek S., Guegan J.-F. Artificial Neuronal Networks: Application to Ecology and Evolution 2000, Springer, Berlin, 262pp.
85. Lek S., Scardi M., Verdonschot P., Descy J.-P., Park Y.-S. Modelling Community Structure in Freshwater Ecosystems 2005, Springer Verlag, Berlin, 518pp.
86. Lek-Ang S., Park Y., Ait-Mouloud S., Deharveng L. Collembolan communities in a peat bog versus surrounding forest analyzed by using self-organizing map. Ecol. Model. 2007, 203:9-17.
87. Lemly A.D., Smith R.J.F. A behavioral assay for assessing effects of pollutants on fish chemoreception. Ecotox. Environ. Saf. 1986, 11:210-218.
88. Lencioni V., Maiolini B., Marziali L., Lek S., Rossaro B. Macroinvertebrate assemblages in glacial stream systems: a comparison of linear multivariate methods with artificial neural networks. Ecol. Model. 2007, 203:119-131.
89. Lepetz D., Némoz-Gaillard M., Aupetit M. Concerning the differentiability of the energy function in vector quantization algorithms. Neural Netw. 2007, 20:621-630.
90. Lin G., Wu M. A hybrid neural network model for typhoon-rainfall forecasting. J. Hydrol. 2009, 375:450-458.
91. Lo Z.-P., Bavarian B. On the rate of convergence in topology preserving neural networks. Biol. Cybern. 1991, 65:55-63.
92. Lu R.-S., Lo S.-L. Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Res. 2002, 36:2265-2274.
93. Mangiameli P., Chen S.W., West D. A comparison of som neural network and hierarchical clustering methods. Eur. J. Oper. Res. 1996, 93:402-417.
94. Manolakos E., Virani H., Novotny V. Extracting knowledge on the links between the water body stressors and biotic integrity. Water Res. 2007, 41:4041-4050.
95. Marsland S., Shapiro J., Nehmzow U. A self-organizing neural network that grows when required. Neural Netw. 2002, 15:1041-1058.
96. Mele P., Crowley D. Application of self-organizing maps for assessing soil biological quality. Agric. Ecosyst. Environ. 2008, 126:139-152.
97. Melssen W., Wehrens R., Buydens L. Supervised Kohonen networks for classification problems. Chemom. Intell. Lab. Syst. 2006, 83:99-113.
98. Melssen, W., Ustün, B., Buydens, L., 2007. SOMPLS: A supervised self-organising map-partial least squares algorithm for multivariate regression problems. 86, 102-120.
99. Mitra, A., 1994. Dynamic externalities and industrial location. Brown University mimeo.
100. Morin S., Bottin M., Mazzella N., Macary F., Delmas F., Winterton P., Coste M. Linking diatom community structure to pesticide input as evaluated through a spatial contamination potential (Phytopixal): a case study in the Neste river system (South-West France). Aquat. Toxicol. 2009, 94:28-39.
101. Moshou D., Bravo C., Oberti R., West J., Bodria L., McCartney A., Ramon H. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging 2005, 11:75-83.
102. Mostafa M.M. Shades of green: a psychographic segmentation of the green consumer in Kuwait using self-organizing maps. Expert Syst. Appl. 2009, 36:11030-11038.
103. Nikolic N., Park Y.-S., Sancristobal M., Lek S., Chevalet C. What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations. Genet. Res. Camb. 2009, 91:121-132.
104. Nishiyama K., Endo S., Jinno K., Bertacchi Uvo C., Olsson J., Berndtsson R. Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map. Atmos. Res. 2007, 83:185-200.
105. Obach M., Wagner R., Werner H., Schmidt H. Modelling population dynamics of aquatic insects with artificial neural networks. Ecol. Model. 2001, 146:207-217.
106. Odum H.T. Systems Ecology 1983, Wiley, New York, 644pp.
107. Odum E.P., Barrett G.W. Fundamentals of Ecology 2005, Thomson Brooks/Cole, Belmont, 598pp.
108. Oh H.-M., Ahn C.-Y., Lee J.-W., Chon T.-S., Choi K.H., Park Y.-S. Community patterning and identification of predominant factors in algal bloom in Daechung Reservoir (Korea) using artificial neural networks. Ecol. Model. 2007, 203:109-118.
109. Özesmi S.L., Özesmi U. An antificial neural network approach to spatial habitat modeling with interspecific interaction. Ecol. Model. 1999, 116:15-31.
110. Paini D.R., Worner S.P., Cook D.C., De Barro P.J., Thomas M.B. Using a self-organizing map to predict invasive species: sensitivity to data errors and a comparison with expert opinion. J. Appl. Ecol. 2010, 47:290-298.
111. Parikh J., DaPonte J., Vitale J., Tselioudis G. An evolutionary system for recognition and tracking of synoptic-scale storm systems. Pattern Recognit. Lett. 1999, 20:1389-1396.
112. Park Y.-S., Chung Y.-J. Hazard rating of pine trees from a forest insect pest using artificial neural networks. For. Ecol. Manage. 2006, 222:222-233.
113. Park Y.-S., Cang J., Sovan L., Cao W., Brosse S. Conservation strategies for endemic fish species threatened by the Three Goges Dam. Conserv. Biol. 2003, 17:1748-1785.
114. Park Y.-S., Céréghino R., Compin A., Lek S. Applications of artificial neural networks for patterning and predictiong aquatic insect species richness in running waters. Ecol. Model. 2003, 160:265-280.
115. Park Y.-S., Chon T.-S., Kwak I.-S., Lek S.-H. Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks. Sci. Total Environ. 2004, 327:105-122.
116. Park Y.-S., Chung N.-I., Choi K.-H., Cha E.Y., Lee S.-K., Chon T.-S. Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon. Aquat. Toxicol. 2005, 71:215-228.
117. Park Y.-S., Lek S., Scardi M., Verdonschot P.F.M., Jørgensen S.E. Patterning exergy of benthic macroinvertebrate communities using self-organizing maps. Ecol. Model. 2006, 195:105-113.
118. Park Y.-S., Tison J., Lek S., Giraudel J.-L., Coste M., Delmas F. Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecol. Inform. 2006, 3:247-257.
119. Park Y.-S., Song M.-Y., Park Y.-C., Oh K.-H., Cho E., Chon T.-S. Community patterns of benthic macroinvertebrates collected on the national scale in Korea. Ecol. Model. 2007, 203:26-33.
120. Pascual-Marqui R.D., Pascual-Montano A., Kochi K., Carazo J.M. Smoothly distributed fuzzy c-means: a new self-organizing map. Pattern Recognit. 2001, 34:2395-2402.
121. Penczak T., Kruk A., Park Y.-S., Lek S. Patterning spatial variation in fish assemblage structures and diversity in the Pilica River system using a self-organizing map (SOM). Modelling Community Structure in Freshwater Ecosystems 2005, 100-113. Springer-Verlag, Berlin. Y.S. Park, S. Lek (Eds.).
122. Penczak T., Kruk A., Grzybkowska M., Dukowska M. Patterning of impoundment impact on chironomid assemblages and their environment with use of the self-organizing map (SOM). Acta Oecol. 2006, 30:312-321.
123. Pisati M., Whelan C., Lucchini M., Maitre B. Mapping patterns of multiple deprivation using self-organising maps: an application to EU-SILC data for Ireland. Soc. Sci. Res. 2009, 29:405-418.
124. Prieto B., Tricas F., Merelo J., Mora A., Prieto A. Visualizing the evolution of a web-based social network. J. Netw. Comput. Appl. 2008, 31:677-698.
125. Recknagel F. Ecological Informatics 2nd. Scope, Techniques and Applications 2006, Springer, New York, 496pp.
126. Recknagel F., Cao H., Kim B., Takamura N., Welk A. Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation. Ecol. Inform. 2006, 1:133-151.
127. Recknagel F., Talib A., Van den Molen D. Phytoplankton community dynamics of two adjacent Dutch lakes in response to seasons and eutrophication control unravelled by non-supervised artificial neural networks. Ecol. Inform. 2006, 1:277-285.
128. Ressom H., Wang D., Natrarajan P. Adaptive double self-organizing maps for clustering gene expression profiles. Neural Netw. 2003, 16:633-640.
129. Reusch D. Nonlinear climatology and paleoclimatology: capturing patterns of variability and change with Self-Organizing Maps. Phys. Chem. Earth 2010, 35:329-340.
130. Richardson A.J., Risien C., Shillington Using self-organizing maps to identify patterns in satellite imagery. Prog. Oceanogr. 2003, 223-239.
131. Rimet F., Druart J., Anneville O. Exploring the dynamics of plankton diatom communities in Lake Geneva using emergent self-organizing maps (1974-2007). Ecol. Inform. 2009, 4:99-110.
132. Ritter H., Schulten K. Topology conserving mappings for learning motor tasks. Neural networks for computing, AIP Conf. Proc. 1986, 151:376-380. J.S. Denker (Ed.).
133. Ritter H., Schulten K. Convergency properties of Kohonen's topology conserving maps: fluctuations, stability and dimension selection. Biol. Cybern. 1988, 60:59-71.
134. Ritter H., Martinetz T., Schulten K. Neural Computation and Self-Organizing Maps: An Introduction 1992, Addison-Wesley, Reading, MA, 300pp.
135. Rousset P., Guinot C., Maillet B. Understanding and reducing variability of SOM neighbourhood structure. Neural Netw. 2006, 19:838-846.
136. Roux O., Gevrey M., Arvanitakis L., Gers C., Bordat D., Legal L. ISSR-PCR: Tool for discrimination and genetic structure analysis of Plutella xylostella populations native to different geographical areas. Mol. Phylogen. Evolut. 2007, 43:240-250.
137. Ruggiero A., Cereghino R., Figuerola J., Marty P., Angelibert S. Farm ponds make a contribution to the biodiversity of aquatic insects in a French agricultural landscape. CR Biol. 2008, 331:298-308.
138. Saltelli A., Tarantola S., Campolongo F., Ratto U. Sensitivity Analysis in Practice 2004, Wiley, Chichester, 219pp.
139. Sánchez-Martos F., Aguilera P.A., Garrido-Frenich A., Torres J.A., Pulido-Bosch A. Assessment of groundwater quality by means of self-organizing maps: application in a semiarid area. J. Environ. Manage. 2002, 30:716-726.
140. Scardi M. Artificial neural networks as empirical models for estimating phytoplankton production. Mar. Ecol. Prog. Ser. 1996, 139:289-299.
141. Shanmuganathan S., Sallis P., Buckeridge J. Self-organising map methods in integrated modeling of environmental and economic systems. Environ. Model. Softw. 2006, 21:1247-1256.
142. Similä T. Self-organizing map visualizing conditional quantile functions with multidimensional covariates. Comput. Stat. Data Anal. 2006, 50:2097-2110.
143. Simon G., Lee J., Cottrell M., Verleysen M. Forecasting the CATS benchmark with the Double Vector Quantization method. Neurocomputing 2007, 70:2400-2409.
144. Son K.-H., Ji Y.-M., Park Y.-M., Cui Y., Wang H.Z., Chon T.-S., Cha E.Y. Recurrent Self-Organizing Map implemented to detection of temporal line-movement patterns of Lumbriculus variegates (Oligochaeta: Lumbriculidae) in response to the treatments of heavy metal. Transaction on Biomedicine and Health Vol 10 Environemtal Toxicology. Southanpton 2006, 77-91. WIT Press.
145. Song M.-Y., Lee S.-E., Park J., Park J., Kim B., Koh S., Lee K., Park Y.-S., Chon T.-S. Comparative community analysis of benthic macroinvertebrates and microorganisms across different levels of organic pollution in a stream by using artificial neural networks. WSEAS Ttrans. Biol. Biomed. 2005, 3:257-268.
146. Song M.-Y., Park Y.-S., Kwak I.-S., Woo H., Chon T.-S. Characterization of benthic macroinvertebrate communities in a restored stream by using self-organizing map. Ecol. Inform. 2006, 1:295-305.
147. Song M.-Y., Hwang H.-J., Kwak I.-S., Ji C.-W., Oh Y.-N., Youn B.-J., Chon T.-S. Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. Ecol. Model. 2007, 203:18-25.
148. Sørensen P.B., Giralt F., Rallo R., Espinosa G., Münier B., Gylenkærne S., Thomsen M. Conscious worst case definition for risk assessment, part II A methodological case study for pesticide risk assessment. Sci. Total Environ. 2010, 208:3860-3870.
149. Stone G., Barnes J., Montgomery C. Ecoscale: a scale for the measurement of environmentally responsible consumers. Psychol. Market. 1995, 12:595-612.
150. Strickert M., Hammer B. Merge SOM for temporal data. Neurocomputing 2005, 64:39-71.
151. Strickert M., Hammer B., Blohm S. Unsupervised recursive sequences processing. Neurocomputing 2005, 63:69-98.
152. Suganthan P. Pattern classification using multiple hierarchical overlapped self-organizing maps. Pattern Recognit. 2001, 34:2173-2179.
153. Tison J., Park Y.-S., Coste M., Wasson J.G., Ector L., Rimet F., Delmas F. Typology of diatom communities and the influence of hydro-ecoregions: a study on the French hydrosystem scale. Water Res. 2005, 39:3177-3188.
154. Tison J., Park Y.-S., Coste M., Wasson J.G., Rimet F., Ector L., Delmas F. Predicting diatom reference communities at the French hydrosystem scale: a first step towards the definition of the good ecological status. Ecol. Model. 2007, 203:99-108.
155. Tokunaga K., Furukawa T. Modular network SOM. Neural Netw. 2009, 22:82-90.
156. Tolat V.V. An analysis of Kohonen's self-organizing maps using a system of energy functions. Biol. Cybern. 1990, 64:155-164.
157. Tran L.T., Knight C.G., O'Neill R.V., Smith E.R., O'Connell M. Self-organizing maps for integrated environmental assessment of the Mid-Atlantic Region. Environ. Manage. 2003, 31:822-835.
158. Tszydel M., Grzybkowska M., Kruk A. Influence of dam removal on trichopteran assemblages in the lowland Drzewiczka River, Poland. Hydrobiology 2009, 630:75-89.
159. Ultsch A. Self-organizing neural networks for visualization and classification. Information and Classification 1993, 307-313. Berlin, Springer-Verlag. O. Opitz, B. Lausen, R. Klar (Eds.).
160. Vančo P., Farkaš I. Experimental comparison of recursive self-organizing maps for processing tree-structured data. Neurocomputing 2010, 73:1362-1375.
161. Vesanto J. SOM-based data visualization methods. Intell. Data Analsis 1999, 3:111-126.
162. Vesanto J., Alhoniemi E. Clustering of the Self-Organizing Map. IEEE Trans. Neural Netw. 2000, 11:586-600.
163. Villmann T., Bauer H.U. Applications of the growing self-organizing map1. Neurocomputing 1998, 21:91-100.
164. Voegtlin T. Context quantization and contextual self-organizing maps. Proc. Int. Joint Conf. on Neural Networks 2000, 5:20-25.
165. Voegtlin T., Dominey P. Learning high-degree sequences in a linear network. Proceedings of the IJCNN'2001 2001, 1:940-944.
166. Walley W.J., O'Connor M.A. Unsupervised pattern recognition for the interpretation of ecological data. Ecol. Model. 2001, 146:219-230.
167. Wang K., Salhi A., Fraga E.S. Process design optimization using embedded hybrid visualization and data analysis technique within a genetic algorithm optimization framework. Chem. Eng. Process. 2004, 43:663-675.
168. Ward J.H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 1963, 58:236-244.
169. Wong J.W.H., Cartwright H.M. Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets. J. Biomed. Inform. 2005, 38:322-330.
170. Wong H.-S., Yang Sha B.M., Ip H.H.S. 3D head model retrieval in kernel feature space using HSOM. Pattern Recognit. 2008, 41:468-483.
171. Wu X.-L., Griffin K.B., Garcia M.D., Michal J.J., Xiao Q., Wright R.W., Jiang Z. Census of orthologous genes and self-organizing maps of biologically relevant transcriptional patterns in chickens (Gallus gallus). Gene 2004, 340:213-225.
172. Wu L.-Y., Li Z., Wang R.-S., Zhang X.-S., Chen L. Self-organizing map approaches for the haplotype assembly problem. Math. Comput. Simul 2009, 79:3026-3037.
173. Xia Z., Rui S., Bing Z., Qingxi T. Land cover classification of the North China Plain using MODIS EVI time series. ISPRS J. Photogramm. 2008, 63:476-484.
174. Xiao X., Shao S., Ding Y., Huang Z., Huang Y., Choum K.C. Using complexity measure factor to predict protein subcellular location. Amino Acids 2005, 28:57-61.
175. Xu K., Xu Y., Chow T.W.S. PolSOM: a new method for multidimensional data visualization. Pattern Recognit. 2010, 43:1668-1675.
176. Yin H. Data visualization and manifold mapping using the ViSOM. Neural Netw. 2002, 15:1005-1016.
177. Yin H. On multidimensional scaling and the embedding of self-organizing maps. Neural Netw. 2008, 21:160-169.
178. Zhang J., Dong Y., Xi Y. A comparison of SOFM ordination with DCA and PCA in gradient analysis of plant communities in the midst of Taihang Mountains. China. Ecol. Inform. 2008, 3:367-374.
179. Zhang L., Scholz M., Mustafa A., Harrington R. Assessment of the nutrient removal performance in integrated constructed wetlands with the self-organizing map. Water Res. 2008, 42:3519-3527.
180. Zhang L., Scholz M., Mustafa A., Harrington R. Application of the self-organizing map as a predition tool for an integrated constructed wetland agroecosystem treating agricultural runoff. Bioresour. Technol. 2009, 100:559-565.
181. Zurada J.M. Introduction to Artificial Neural Systems 1992, West Publishing Company, 683pp.