Cluster analysis applied to regional geochemical data: Problems and possibilities

Applied Geochemistry - Tập 23 - Trang 2198-2213 - 2008
Matthias Templ1,2, Peter Filzmoser1, Clemens Reimann3
1Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstrasse 8-10, A-1040 Wien, Austria
2Department of Register, Classification and Methodology, Statistics Austria, Guglgasse 13, A-1040 Wien, Austria
3Geological Survey of Norway, N-7491 Trondheim, Norway

Tài liệu tham khảo

Aitchison, 1986 Aitchison, 1992, On criteria for measures of compositional difference, Math. Geol., 24, 365, 10.1007/BF00891269 Aitchison, 2000, Logratio analysis and compositional distance, Math. Geol., 32, 271, 10.1023/A:1007529726302 Aitchison, 2003 Aitchison, 2000, Logratio analysis and compositional distance, Math. Geol., 32, 257, 10.1023/A:1007529726302 Bandemer, 1992 Barcelo-Vidal, 1996, Some aspects of transformations of compositional data and the identification of outliers, Math. Geol., 28, 501, 10.1007/BF02083658 Bezdek, 1981 Box, 1964, An analysis of transformations, J. Roy. Statist. Soc. B, 26, 211 Breiman, 1996, Bagging predictors, Machine Learning, 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 2006 Butler, 1976, Principal components analysis using the hypothetical closed array, Math. Geol., 8, 25, 10.1007/BF01039682 Calinski, 1974, A dendrite method for cluster analysis, Commun. Statist., 3, 1, 10.1080/03610927408827101 Cohen, 1991 Davis, 1973 Dempster, 1977, Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. Roy. Statist. Soc. B, 39, 1 Dunn, 1973, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybernetics, 3, 32, 10.1080/01969727308546046 Egozcue, 2003, Isometric logratio transformations for compositional data analysis, Math. Geol., 35, 279, 10.1023/A:1023818214614 Everitt, 1974 Filzmoser, 2008, Outlier detection for compositional data using robust methods, Math. Geol., 40, 233 Filzmoser, 2005, Multivariate outlier detection in exploration geochemistry, Comput. Geosci., 31, 579, 10.1016/j.cageo.2004.11.013 Fraley, 1998, How many clusters? Which clustering method? Answers via model-based cluster analysis, Comput. J., 41, 578, 10.1093/comjnl/41.8.578 Fraley, 2002, Model-based clustering, discriminant analysis and density estimation, J. Am. Statist. Assoc., 97, 611, 10.1198/016214502760047131 Frapporti, 1996, Trace element in the shallow groundwater of the Netherlands. A geochemical and statistical interpretation for the nation monitoring network data, Aquat. Geochem., 2, 51, 10.1007/BF00240853 Friedman, 2004, Clustering objects on subsets of attributes (with discussion), J. Roy. Statist. Soc. B, 66, 815, 10.1111/j.1467-9868.2004.02059.x Gath, 1989, Unsupervised optimal fuzzy clustering, IEEE Trans. Pattern Anal. Intelligence, 11, 773, 10.1109/34.192473 Gordon, 1999 Gower, 1966, Some distance properties of latent root and vector methods used in multivariate analysis, Biometrika, 53, 325, 10.1093/biomet/53.3-4.325 Gustafson, 1979, Fuzzy clustering with a fuzzy covariance matrix, Proceedings of the IEEE-CDC, 2, 761 Haldiki, 2002, Cluster validity methods, SIGMOD Record, 31, 40, 10.1145/565117.565124 Hartigan, 1975 Helsel, 1990, Less than obvious: statistical treatment of data below the detection limit, Environ. Sci. Technol., 24, 1767, 10.1021/es00082a001 Helsel, 2004 Hubert, 1985, Comparing partitions, J. Classif., 2, 193, 10.1007/BF01908075 Ji, 2007, Semi-hierarchical correspondence cluster analysis and regional geochemical pattern recognition, J. Geochem. Explor., 93, 109, 10.1016/j.gexplo.2006.10.002 Ji, 1995, Correspondence cluster analysis and its application in exploration geochemistry, J. Geochem. Explor., 55, 137, 10.1016/0375-6742(95)00025-9 Kaufman, 1990 Leisch, F., 1999. Bagged clustering. Working Paper 51, SFB Adaptive Information Systems and Modeling in Economics and Management Science, Wirtschaftsuniversität Wien, Austria. Leisch, 2006, A toolbox for k-centroids cluster analysis, Comput. Statist. Data Anal., 51, 526, 10.1016/j.csda.2005.10.006 Le Maitre, 1982 MacQueen, 1967 Martinetz, 1993, Neural-gas network for vector quantization and its application to time-series prediction, IEEE Trans. Neural Networks, 4, 558, 10.1109/72.238311 R Development Core Team, 2006. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Raftery, A.E., Dean, N., 2004. Variable selection for model-based clustering. Technical Report 452, Department of Statistics, University of Washington. Reimann, 2000, Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data, Environ. Geol., 39, 1001, 10.1007/s002549900081 Reimann, 2007, Element contents in leaves of four plant species (birch, mountain ash, fern and spruce) along anthropogenic and geogenic concentration gradients, Sci. Total Environ., 377, 416, 10.1016/j.scitotenv.2007.02.011 Reimann, 2006, The influence of a city on element contents of a terrestrial moss (Hylocomium splendens), Sci. Total Environ., 369, 419, 10.1016/j.scitotenv.2006.04.026 Reimann, 2007, Element contents in birch leaves, bark and wood under different anthropogenic and geogenic conditions, Appl. Geochem., 22, 1549, 10.1016/j.apgeochem.2007.03.048 Reimann, 1998 Reimann, 2002, Factor analysis applied to regional geochemical data: problems and possibilities, Appl. Geochem., 17, 185, 10.1016/S0883-2927(01)00066-X Ripley, 1996 Rock, 1988 Sanford, 1993, An objective replacement method for censored geochemical data, Math. Geol., 25, 59, 10.1007/BF00890676 Templ, M., 2003. Cluster Analysis applied to Geochemical Data. Diploma Thesis, Vienna University of Technology, Vienna, Austria. Ward, 1963, Hierarchical grouping to optimize an objective function, J. Am. Statist. Assoc., 58, 236, 10.2307/2282967 Yeung, 2001, An empirical study on principal component analysis for clustering gene expression data, Bioinformatics, 17, 763, 10.1093/bioinformatics/17.9.763