Chemometrical exploration of the wet precipitation chemistry from the Austrian Monitoring Network (1988-1999)

Journal of Environmental Management

Volumn 74, Issue 4, 2005, Pages 349-363

  Stanimirova, I ^a   Daszykowski, M ^a   Massart, D L ^a   Questier, F ^a   Simeonov, V ^b   Puxbaum, H ^c  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^b UNIVERSITY OF SOFIA   (Bulgaria)

^c VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

DBSCAN; Density based clustering; Environmetrics; Kohonen network; Neural gas network; OPTICS

Indexed keywords

CLASSIFICATION; MEASUREMENT METHOD; PARTITIONING; PRECIPITATION (CHEMISTRY);

ARTICLE; AUSTRIA; CHEMICAL ANALYSIS; CHEMOMETRIC ANALYSIS; CLUSTER ANALYSIS; COMPUTER PROGRAM; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; ENVIRONMENTAL MONITORING; GEOGRAPHY; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; PRECIPITATION; PRINCIPAL COMPONENT ANALYSIS; VISUAL AID;

AUSTRIA; CENTRAL EUROPE; EASTERN HEMISPHERE; EURASIA; EUROPE; WORLD;

EID: 14544308779     PISSN: 03014797     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jenvman.2004.08.018     Document Type: Article

References (29)

1. Ankerst, M., Breunig, M., Kriegel, H., Sander, J., 1999. OPTICS: ordering points to identify the clustering structure. Proceedings on ACM SIGMOD'99, International Conference on Management of Data, Philadelphia, PA.
2. R. Beale T. Jackson Neural Computing: An Introduction 1992 IOP Publishing Press Bristol
3. BMGU, 1984. Richtlinie 11, Immissionsmessung des nassen Niederschlags und des sedimentierten Staubes, Luftverunreinigung - Immissionsmessung. Bundesministerium für Gesundheit und Umweltschutz, Vienna, Austria.
4. Croux C., Ruiz-Gazen A., 1996. A fast algorithm for robust principal components based on Projection Pursuit, COMPSTAT: Proceedings in Computational Statistics 1996. Physica-Verlag, Heidelberg, pp. 211-217.
5. M. Daszykowski B. Walczak D.L. Massart Looking for natural patterns in data. Part 1. Density-based approach Chemom. Intell. Lab. Syst. 56 2001 83-92
6. M. Daszykowski B. Walczak D.L. Massart Looking for natural patterns in analytical data. Part 2. Tracing local density J. Chem. Inform. Comput. Sci. 42 2002 500-5007
7. Ester, M., Kriegel, H., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96).
8. Q. Guo W. Wu F. Questier D.L. Massart C. Bucon S. de Jong Sequential projection pursuit using genetic algorithms for data mining of analytical data Anal. Chem. 72 2000 2846-2855
9. P. Huber Projection Pursuit Ann. Statist. 13 2 1985 435-475
10. M. Hubert P. Rousseeuw S. Verboven A fast method for robust principal components with application to chemometrics Chemometrics and Intelligent Laboratory Systems 60 2002 101-111
11. M.C. Jones R. Sibson What is projection pursuit? (with discussion) J. R. Statist. Soc. A 150 1987 1-36
12. M.F. Kalina H. Puxbaum A high density network for wet only precipitation chemistry sampling in Austria Q. J. Hung. Meteorol. Service 100 1-3 1996 159-170
13. L. Kaufman P.J. Rousseeuw Finding Groups in Data: An Introduction to Cluster Analysis 1990 Wiley New York
14. T. Kohonen Self-Organisation and Associative Memory 1990 Springer Berlin
15. T. Kohonen Self-Organizing Maps 1997 Springer Berlin
16. J. MacQueen Some methods for classification and analysis of multivariate observations Fifth Berkeley Symp. Math. Statist. Probab. 1 1967 281-297
17. T. Martinetz G. Berkovich K. Schulten 'Neural Gas' network for vector quantization and its application to time series prediction IEEE Trans. Neural Networks 4 4 1993 558-569
18. B. Massart Q. Guo F. Questier D.L. Massart C. Boucon S. de Jong B.G.M. Vandeginste Data structures and data transformations for clustering chemical data Trends Anal. Chem. 20 1 2001 35-41
19. W.J. Melssen J.R. Smits L.M.C. Buydens G. Kateman Tutorial using artificial neural networks for solving chemical problems. Part II. Kohonen self-organising feature maps and Hopfield networks Chemom. Intell. Lab. Syst. 23 1994 267-291
20. F. Questier Q. Guo B. Walczak D.L. Massart C. Boucon S. de Jong The Neural-Gas network for classifying analytical data Chemom. Intell. Lab. Syst. 61 1-2 2002 105-121
21. C. Sanchez P.J. Lewi D.L. Massart Effect of different preprocessing methods for principal component analysis applied to the composition of mixtures: Detection of impurities in HPLC-DAD Chemom. Intell. Lab. Syst. 25 1994 157-177
22. V. Simeonov H. Puxbaum S. Tsakovski C. Sarbu M. Kalina Classification and receptor modeling of wet precipitation data from central Austria (1984-1993) Environmetrics 10 1999 137-152
23. A. Thielemans P.J. Lewi D.L. Massart Similarities and differences among multivariate display techniques illustrated by Belgian cancer mortality distribution data Chemom. Intell. Lab. Syst. 3 1988 277-300
24. S. Tsakovski H. Puxbaum V. Simeonov M. Kalina H. Löffler G. Heimburger P. Biebl A. Weber A. Damm Trend, seasonal and multivariate modeling study of wet precipitation data from the Austrian Monitoring Network (1990-1997) J. Environ. Monit. 2 2000 424-431
25. B.M.G. Vandeginste D.L. Massart L.M.C. Buydens S. de Jong P.J. Lewi J. Smeyers-Verbeke Handbook of Chemometrics and Qualimetrics. Part B 1998 Elsevier Amsterdam
26. W. Vogt D. Nagel H. Sator Cluster Analysis in Clinical Chemistry: A Model 1987 Wiley New York
27. Winkler, P., Jobst, S., Harder, C., 1989. Meteorologische Pruefung und Beurteilung von Sammelgeraeten fuer nasse Deposition (in German). GSF, BTP-Bericht 1/89, Muenchen.
28. I.S. Yenyukov Data Analysis Learning Symbolic and Numeric Knowledge 1989 Nova Science Publishers New York pp. 181-188
29. J. Zupan J. Gasteiger Neural Networks in Chemistry and Drug Design 1999 Wiley-VCH Verlag Weinheim