메뉴 건너뛰기




Volumn , Issue 126, 2012, Pages 2-90

Cluster analysis to understand socio-ecological systems: A guideline

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CLUSTER ANALYSIS; EMPIRICAL ANALYSIS; KNOWLEDGE; PARAMETERIZATION; SOFTWARE;

EID: 84867480556     PISSN: 14360179     EISSN: 18651291     Source Type: Book Series    
DOI: None     Document Type: Article
Times cited : (5)

References (239)
  • 3
    • 33846863801 scopus 로고    scopus 로고
    • Radial Clustergrams: Visualizing the aggregate properties of hierarchical clusters
    • Agrafiotis, D.K., Bandyopadhyay, D., Farnam, M. (2007). Radial Clustergrams: Visualizing the aggregate properties of hierarchical clusters. Journal. Chem. Inf. Model., Vol. 47, 69-75.
    • (2007) Journal. Chem. Inf. Model. , vol.47 , pp. 69-75
    • Agrafiotis, D.K.1    Bandyopadhyay, D.2    Farnam, M.3
  • 5
    • 0029507498 scopus 로고
    • Local indicators of spatial association-LISA
    • Anselin, L. (1995). "Local indicators of spatial association-LISA". Geographical Analysis, 27, 93-115.
    • (1995) Geographical Analysis , vol.27 , pp. 93-115
    • Anselin, L.1
  • 6
    • 33645764129 scopus 로고    scopus 로고
    • Exploring Spatial Data with GeoDATM: A Workbook
    • Anselin, L. (2005). "Exploring Spatial Data with GeoDATM: A Workbook". Spatial Analysis Laboratory. p. 138. http://www.csiss.org/clearinghouse/GeoDa/geodaworkbook.pdf.
    • (2005) Spatial Analysis Laboratory. , pp. 138
    • Anselin, L.1
  • 7
    • 3242889356 scopus 로고    scopus 로고
    • Web-based analytical tools for the exploration of spatial data
    • Anselin, L., Kim, Y.-W., Syabri, I. (2004b). Web-based analytical tools for the exploration of spatial data. Journal of Geographical Systems, 6, 197-218.
    • (2004) Journal of Geographical Systems , vol.6 , pp. 197-218
    • Anselin, L.1    Kim, Y.-W.2    Syabri, I.3
  • 8
    • 0002418085 scopus 로고    scopus 로고
    • Heterogeneity issues in local measurements of spatial association
    • 1996
    • Bao, S., Henry, M.S. (1996). "Heterogeneity issues in local measurements of spatial association. " Geographical Systems, 1996, Vol. 3, 1-13.
    • (1996) Geographical Systems , vol.3 , pp. 1-13
    • Bao, S.1    Henry, M.S.2
  • 12
    • 25144465421 scopus 로고    scopus 로고
    • Self-organizing Maps as Substitutes for K-Means Clustering
    • V.S. Sunderam et al. (Eds.): 2005, LNCS 3516
    • Bação, F., Lobol, V., Painho, M. (2005). Self-organizing Maps as Substitutes for K-Means Clustering. V.S. Sunderam et al. (Eds.): ICCS 2005, LNCS 3516, 476-483, 2005.
    • (2005) ICCS 2005 , pp. 476-483
    • Bação, F.1    Lobol, V.2    Painho, M.3
  • 14
    • 58849142031 scopus 로고    scopus 로고
    • Spectral methods in machine learning and new strategies for very large datasets
    • January 13, 2009
    • Belabbas, M.-A., Wolfe, P.J. (2009a). Spectral methods in machine learning and new strategies for very large datasets. PNAS January 13, 2009, Vol. 106 (2), 369-374.
    • (2009) PNAS , vol.106 , Issue.2 , pp. 369-374
    • Belabbas, M.-A.1    Wolfe, P.J.2
  • 15
    • 73349115535 scopus 로고    scopus 로고
    • On landmark selection and sampling in highdimensional data analysis
    • in Series A, of the Royal Society (2009)
    • Belabbas, M.-A., Wolfe, P.J. (2009b). On landmark selection and sampling in highdimensional data analysis, in Philosophical Transactions, Series A, of the Royal Society 367 (2009), 4295-4312.
    • (2009) Philosophical Transactions , vol.367 , pp. 4295-4312
    • Belabbas, M.-A.1    Wolfe, P.J.2
  • 16
    • 0036359730 scopus 로고    scopus 로고
    • A stability based method for discovering structure in clustered data
    • Ben-Hur, A., Elisieeff, A., Guyon, I. (2002). A stability based method for discovering structure in clustered data. Pac Symp Biocomput. 2002, 6-17.
    • (2002) Pac Symp Biocomput. , vol.2002 , pp. 6-17
    • Ben-Hur, A.1    Elisieeff, A.2    Guyon, I.3
  • 18
    • 0036082940 scopus 로고    scopus 로고
    • VAT: A Tool for Visual Assessment of (Cluster) Tendency
    • IEEE Press, Piscataway, N. J
    • Bezdek, J.C., Hathaway, R.J. (2002). VAT: A Tool for Visual Assessment of (Cluster) Tendency, Proc. IJCNN 2002, IEEE Press, Piscataway, N.J., 2225-2230.
    • (2002) Proc. IJCNN 2002 , pp. 2225-2230
    • Bezdek, J.C.1    Hathaway, R.J.2
  • 19
    • 35348896605 scopus 로고    scopus 로고
    • Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices
    • Bezdek, J.C., Hathaway, R.J., Huband, J.M. (2007). Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices. IEEE Trans. On Fuzzy Systems, Vol. 15 (5), 890-903.
    • (2007) IEEE Trans. On Fuzzy Systems. , vol.15 , Issue.5 , pp. 890-903
    • Bezdek, J.C.1    Hathaway, R.J.2    Huband, J.M.3
  • 22
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • Blum, A.L., Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, Vol. 97, 245-271.
    • (1997) Artificial Intelligence , vol.97 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 23
    • 0037399775 scopus 로고    scopus 로고
    • Cluster validation techniques for genome expression data
    • Bolshakova, N., Azuaje, F. (2003). Cluster validation techniques for genome expression data. Signal Processing 2003, 83, 825-833.
    • (2003) Signal Processing 2003 , vol.83 , pp. 825-833
    • Bolshakova, N.1    Azuaje, F.2
  • 24
    • 19544366640 scopus 로고    scopus 로고
    • A knowledge-driven approach to cluster validity assessment
    • Bolshakova, N., Azuaje, F., Cunningham, P. (2005a). A knowledge-driven approach to cluster validity assessment. Bioinformatics 2005, 21, 2546-2547.
    • (2005) Bioinformatics 2005 , vol.21 , pp. 2546-2547
    • Bolshakova, N.1    Azuaje, F.2    Cunningham, P.3
  • 25
    • 14644403631 scopus 로고    scopus 로고
    • An integrated tool for microarray data clustering and cluster validity assessment
    • Bolshakoval, N., Azuaje, F., Cunningham, P. (2005b). An integrated tool for microarray data clustering and cluster validity assessment. Bioinformatics. Vol. 21 (4), 451-455.
    • (2005) Bioinformatics , vol.21 , Issue.4 , pp. 451-455
    • Bolshakoval, N.1    Azuaje, F.2    Cunningham, P.3
  • 26
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5-32.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 29
    • 62349084208 scopus 로고    scopus 로고
    • A simultaneous learning framework for clustering and classification
    • Cai, W., Chen, S., Zhang, D. (2009). A simultaneous learning framework for clustering and classification. Pattern Recognition, Vol. 42 (7), 1248-1259.
    • (2009) Pattern Recognition. , vol.42 , Issue.7 , pp. 1248-1259
    • Cai, W.1    Chen, S.2    Zhang, D.3
  • 30
    • 0142025120 scopus 로고    scopus 로고
    • Data Dimensionality Estimation Methods: A Survey
    • Elsevier Science, Amsterdam, (2003)
    • Camastra, F. (2003). Data Dimensionality Estimation Methods: A Survey. Pattern Recognition, Vol. 36 (12), 2945-2954, Elsevier Science, Amsterdam, (2003).
    • (2003) Pattern Recognition. , vol.36 , Issue.12 , pp. 2945-2954
    • Camastra, F.1
  • 31
    • 18144401294 scopus 로고    scopus 로고
    • A novel kernel method for clustering
    • Camastra, F., Verri, A. (2005). A novel kernel method for clustering. IEEE Transaction on PAMI, Vol. 27, 801-805.
    • (2005) IEEE Transaction on PAMI , vol.27 , pp. 801-805
    • Camastra, F.1    Verri, A.2
  • 32
  • 33
    • 0029305528 scopus 로고
    • Gaussian parsimonious clustering models
    • Celeux, G., Govaert, G. (1995). Gaussian parsimonious clustering models, Pattern Recognition, 28, 781-793.
    • (1995) Pattern Recognition , vol.28 , pp. 781-793
    • Celeux, G.1    Govaert, G.2
  • 34
    • 0020998698 scopus 로고
    • On Using Principal Components Before Separating a Mixture of two Multivariate Normal Distributions
    • Chang, W.-C. (1983). On Using Principal Components Before Separating a Mixture of two Multivariate Normal Distributions. Applied Statistics, 32, 267-275.
    • (1983) Applied Statistics , vol.32 , pp. 267-275
    • Chang, W.-C.1
  • 36
    • 0030376226 scopus 로고    scopus 로고
    • Measuring the influence of individual data points in a cluster analysis
    • 1432-1343
    • Cheng, R., Milligan, G.W. (1996a). Measuring the influence of individual data points in a cluster analysis. Journal of Classification. Vol. 13 (2), 1432-1343.
    • (1996) Journal of Classification , vol.13 , Issue.2
    • Cheng, R.1    Milligan, G.W.2
  • 39
    • 84898954907 scopus 로고    scopus 로고
    • Spectral kernel methods for clustering
    • Cristianini, N., Shawe-Taylor, J., Kandola, J. (2002). Spectral kernel methods for clustering. In NIPS 14, 2002.
    • (2002) NIPS , vol.14 , pp. 2002
    • Cristianini, N.1    Shawe-Taylor, J.2    Kandola, J.3
  • 41
    • 33947177850 scopus 로고
    • Optimal variable weighting for ultrametric and additive tree clustering
    • De Soete, G. (1986). Optimal variable weighting for ultrametric and additive tree clustering. Quality&Quantity, 20, 169-180.
    • (1986) Quality&Quantity , vol.20 , pp. 169-180
    • de Soete, G.1
  • 42
    • 0000962917 scopus 로고
    • OVWTRE: A program for optimal variable weighting for ultrametric and additive tree fitting
    • De Soete, G. (1988). OVWTRE: A program for optimal variable weighting for ultrametric and additive tree fitting. Journal of Classification, 5, 101-104.
    • (1988) Journal of Classification , vol.5 , pp. 101-104
    • de Soete, G.1
  • 44
    • 54449086895 scopus 로고    scopus 로고
    • Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
    • Donoho, D., Jin, J. (2008). Higher criticism thresholding: Optimal feature selection when useful features are rare and weak. Proceedings of the National Academy of Sciences, Vol. 105, 14790-14795.
    • (2008) Proceedings of the National Academy of Sciences , vol.105 , pp. 14790-14795
    • Donoho, D.1    Jin, J.2
  • 45
    • 73349141108 scopus 로고    scopus 로고
    • Feature selection by higher criticism thresholding achieves the optimal phase diagram
    • Donoho, D., Jin, J. (2009). Feature selection by higher criticism thresholding achieves the optimal phase diagram. Phil Trans R Soc A, 367, 4449-4470.
    • (2009) Phil Trans R Soc A , vol.367 , pp. 4449-4470
    • Donoho, D.1    Jin, J.2
  • 46
    • 29244453931 scopus 로고    scopus 로고
    • On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
    • Drineas, P., Mahoney, M.W. (2005). On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. Journal of Machine Learning Research, Vol. 6, 2153-2175.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 2153-2175
    • Drineas, P.1    Mahoney, M.W.2
  • 47
    • 0037172724 scopus 로고    scopus 로고
    • A prediction-based resampling method for estimating the number of clusters in a dataset
    • Dudoit, S, Fridlyand, J. (2002a). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology, Vol. 3 (7).
    • (2002) Genome Biology , vol.3 , Issue.7
    • Dudoit, S.1    Fridlyand, J.2
  • 48
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discriminant methods for the classification of tumors using gene expression data
    • Dudoit, S., Fridlyand, J., Speed, T.P. (2002b). Comparison of discriminant methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97, 77-87.
    • (2002) J. Am. Stat. Assoc. , vol.97 , pp. 77-87
    • Dudoit, S.1    Fridlyand, J.2    Speed, T.P.3
  • 51
    • 0034909184 scopus 로고    scopus 로고
    • Visualization of expression clusters using Sammonb's non-linear mapping
    • Ewing, R.M., Sherry, J.M. (2001). Visualization of expression clusters using Sammonb's non-linear mapping. Bioinformatics, Vol. 17, 658-659.
    • (2001) Bioinformatics , vol.17 , pp. 658-659
    • Ewing, R.M.1    Sherry, J.M.2
  • 52
    • 34548025132 scopus 로고    scopus 로고
    • A survey of kernel and spectral methods for clustering
    • Filippone, M., Camastra, F. Masulli, F., Rovetta, S. (2007). A survey of kernel and spectral methods for clustering. Pattern Recognition. Vol. 41 (1), 176-190.
    • (2007) Pattern Recognition , vol.41 , Issue.1 , pp. 176-190
    • Filippone, M.1    Camastra, F.2    Masulli, F.3    Rovetta, S.4
  • 53
    • 0013426686 scopus 로고    scopus 로고
    • On the use of self-organizing maps for clustering and visualization
    • Flexer, A. (2001). On the use of self-organizing maps for clustering and visualization. Intelligent Data Analysis, 5, 373-384.
    • (2001) Intelligent Data Analysis , vol.5 , pp. 373-384
    • Flexer, A.1
  • 57
    • 0032269108 scopus 로고    scopus 로고
    • How many clusters? Which clustering method?-Answers via Model-Based Cluster Analysis
    • Fraley, C., Raftery, A.E. (1998). How many clusters? Which clustering method?-Answers via Model-Based Cluster Analysis. Computer Journal, 41, 578-588.
    • (1998) Computer Journal , vol.41 , pp. 578-588
    • Fraley, C.1    Raftery, A.E.2
  • 58
    • 22844453564 scopus 로고    scopus 로고
    • MCLUST: Software for model-based clustering
    • Fraley, C., Raftery, A.E. (1999). MCLUST: Software for model-based clustering. Journal of Classification, 16, 297-306.
    • (1999) Journal of Classification , vol.16 , pp. 297-306
    • Fraley, C.1    Raftery, A.E.2
  • 60
    • 0742306126 scopus 로고    scopus 로고
    • Enhanced model-based clustering, density estimation and discriminant analysis software: MCLUST
    • Fraley, C., Raftery, A.E. (2003). Enhanced model-based clustering, density estimation and discriminant analysis software: MCLUST. Journal of Classification, 20, 263-296.
    • (2003) Journal of Classification , vol.20 , pp. 263-296
    • Fraley, C.1    Raftery, A.E.2
  • 62
    • 0348143190 scopus 로고    scopus 로고
    • Scoring clustering solutions by their biological relevance
    • Gat-Viks, I., Sharan, R., Shamir, R. (2003). Scoring clustering solutions by their biological relevance. Bioinformatics, Vol. 19 (18), 2381-2389.
    • (2003) Bioinformatics. , vol.19 , Issue.18 , pp. 2381-2389
    • Gat-Viks, I.1    Sharan, R.2    Shamir, R.3
  • 63
    • 0000960565 scopus 로고
    • The contiguity ratio and statistical mapping
    • Geary, R. (1954). The contiguity ratio and statistical mapping. Incorporated Statistician, Vol. 5, 115-145.
    • (1954) Incorporated Statistician , vol.5 , pp. 115-145
    • Geary, R.1
  • 64
    • 28744458859 scopus 로고    scopus 로고
    • Bioconductor: Open software development for computational biology and bioinformatics
    • 2004
    • Gentleman, R.C., et al. (2004). Bioconductor: open software development for computational biology and bioinformatics, Genome Biology, 2004, 5:R80.
    • (2004) Genome Biology , vol.5
    • Gentleman, R.C.1
  • 65
    • 84977363017 scopus 로고
    • The analysis of spatial association by use of distance statistics
    • Getis, A., Ord, J.K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, Vol. 24 (3), 189-206.
    • (1992) Geographical Analysis. , vol.24 , Issue.3 , pp. 189-206
    • Getis, A.1    Ord, J.K.2
  • 66
    • 0000028350 scopus 로고    scopus 로고
    • Local spatial statistics: An overview
    • In: P. Longley and M. Batty (eds.) (Cambridge: Geoinformation International)
    • Getis, A., Ord, J.K. (1996). Local spatial statistics: an overview. In: P. Longley and M. Batty (eds.) "Spatial analysis: modeling in a GIS environment" (Cambridge: Geoinformation International), 261-277.
    • (1996) Spatial analysis: Modeling in a GIS environment , pp. 261-277
    • Getis, A.1    Ord, J.K.2
  • 68
    • 0036565280 scopus 로고    scopus 로고
    • Mercer Kernel-Based Clustering in Feature Space
    • Girolami, M. (2002). Mercer Kernel-Based Clustering in Feature Space. IEEE Transactions on Neural Networks, 13 (3), 780-784.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.3 , pp. 780-784
    • Girolami, M.1
  • 69
    • 0037062448 scopus 로고    scopus 로고
    • Community structure in social and biological networks
    • June 11, 2002
    • Girvan, M., Newman, M.E.J. (2002). Community structure in social and biological networks. PNAS June 11, 2002, Vol. 99 (12), 7821-7826.
    • (2002) PNAS , vol.99 , Issue.12 , pp. 7821-7826
    • Girvan, M.1    Newman, M.E.J.2
  • 71
    • 34447501685 scopus 로고    scopus 로고
    • Better alternatives to current methods of scaling and weighting data for cluster analysis
    • Gnanadesikan, R., Kettenring, J.R., Maloor, S. (2007). Better alternatives to current methods of scaling and weighting data for cluster analysis. Journal of Statistical planning and Inference, Vol. 137, 3483-3496.
    • (2007) Journal of Statistical planning and Inference , vol.137 , pp. 3483-3496
    • Gnanadesikan, R.1    Kettenring, J.R.2    Maloor, S.3
  • 72
    • 58349089453 scopus 로고    scopus 로고
    • Consensus Clustering Algorithms: Comparison and Refinement
    • San Francisco, January 19, 2008. Society for Industrial and Applied Mathematics
    • Goder, A., Filkov, V. (2008). Consensus Clustering Algorithms: Comparison and Refinement. Proceedings of the Ninth Workshop on Algorithm Engineering and Experiments (ALENEX)-San Francisco, January 19, 2008. Society for Industrial and Applied Mathematics.
    • (2008) Proceedings of the Ninth Workshop on Algorithm Engineering and Experiments (ALENEX)
    • Goder, A.1    Filkov, V.2
  • 73
    • 0004241258 scopus 로고    scopus 로고
    • (2nd edition). Chapman & Hall/CRC, Boca Raton. Fl
    • Gordon, A.D. (1999). Classification. (2nd edition). Chapman & Hall/CRC, Boca Raton. Fl.
    • (1999) Classification.
    • Gordon, A.D.1
  • 74
  • 75
    • 0037406313 scopus 로고    scopus 로고
    • Validation indices for graph clustering
    • Günter, S, Bunke, H. (2003). Validation indices for graph clustering. Pattern Recognition Letters, Vol. 24, 1107-1113.
    • (2003) Pattern Recognition Letters , vol.24 , pp. 1107-1113
    • Günter, S.1    Bunke, H.2
  • 80
    • 45349097076 scopus 로고    scopus 로고
    • Statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data
    • Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Morris, M. (2009). statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data. J Stat Softw. 2008, 24 (1), 1548-7660.
    • (2009) J Stat Softw. 2008 , vol.24 , Issue.1 , pp. 1548-7660
    • Handcock, M.S.1    Hunter, D.R.2    Butts, C.T.3    Goodreau, S.M.4    Morris, M.5
  • 85
    • 33845331424 scopus 로고    scopus 로고
    • Multiobjective clustering and cluster validation
    • In Multiobjective machine learning edited by Yaochu Jin
    • Handl, J., Knowles, J. (2006a). Multiobjective clustering and cluster validation. In Multiobjective machine learning edited by Yaochu Jin. Springer Series on Computational Intelligence 16, 21-47.
    • (2006) Springer Series on Computational Intelligence , vol.16 , pp. 21-47
    • Handl, J.1    Knowles, J.2
  • 86
    • 34248370390 scopus 로고    scopus 로고
    • Feature subset selection in unsupervised learning via multiobjective optimization
    • Handl, J., Knowles, J. (2006b). Feature subset selection in unsupervised learning via multiobjective optimization. International Journal of Computational Intelligence Research, 2 (3), 217-238.
    • (2006) International Journal of Computational Intelligence Research , vol.2 , Issue.3 , pp. 217-238
    • Handl, J.1    Knowles, J.2
  • 88
    • 25144456056 scopus 로고    scopus 로고
    • Computational cluster validation in postgenomic data analysis
    • Handl, J., Knowles, J., Kell, D.B. (2005). Computational cluster validation in postgenomic data analysis. Bioinformatics, Vol. 21 (15), 3201-3212.
    • (2005) Bioinformatics. , vol.21 , Issue.15 , pp. 3201-3212
    • Handl, J.1    Knowles, J.2    Kell, D.B.3
  • 91
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G.E., Salakhutdinov, R.R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504-507.
    • (2006) Science , vol.313 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 95
    • 52949154587 scopus 로고    scopus 로고
    • An Algorithm for Clustering Tendency Assessment
    • 2008
    • Hu, Y., Hathaway, R.J. (2008). An Algorithm for Clustering Tendency Assessment. WSEAS TRANSACTIONS on MATHEMATICS, Vol. 7 (7), 441-450, 2008.
    • (2008) WSEAS TRANSACTIONS on MATHEMATICS. , vol.7 , Issue.7 , pp. 441-450
    • Hu, Y.1    Hathaway, R.J.2
  • 101
    • 11244326960 scopus 로고    scopus 로고
    • Clustering Visualizations of Multidimensional Data
    • Hurley, C.B. (2004). Clustering Visualizations of Multidimensional Data, Journal of Computational & Graphical Statistics, Vol. 13 (4), 788-806.
    • (2004) Journal of Computational & Graphical Statistics , vol.13 , Issue.4 , pp. 788-806
    • Hurley, C.B.1
  • 103
    • 47249125437 scopus 로고    scopus 로고
    • INCA: New statistic for estimating the number
    • Irigoien, I., Arenas, C. (2008). INCA: New statistic for estimating the number. Statist. Med., 27, 2948-2973.
    • (2008) Statist. Med. , vol.27 , pp. 2948-2973
    • Irigoien, I.1    Arenas, C.2
  • 105
    • 0342990451 scopus 로고    scopus 로고
    • Disease clustering for uncertain locations
    • In: A.B. Lawson, A. Biggeri, D. Bohning, E. Lesaffre, J.-F. Viel, and R. Bertollini, eds. New York: John Wiley & Sons
    • Jacquez, G.M., Jacquez, J.A. (1999). Disease clustering for uncertain locations. In: Disease mapping and risk assessment for public health. A.B. Lawson, A. Biggeri, D. Bohning, E. Lesaffre, J.-F. Viel, and R. Bertollini, eds. New York: John Wiley & Sons.
    • (1999) Disease mapping and risk assessment for public health.
    • Jacquez, G.M.1    Jacquez, J.A.2
  • 106
    • 66749141556 scopus 로고    scopus 로고
    • Spatial Cluster Analysis
    • Chapter 22 In S. Fotheringham and J. Wilson (Eds.). Blackwell Publishing
    • Jacquez, G.M. (2008). Spatial Cluster Analysis. Chapter 22 In "The Handbook of Geographic Information Science", S. Fotheringham and J. Wilson (Eds.). Blackwell Publishing, 395-416.
    • (2008) The Handbook of Geographic Information Science , pp. 395-416
    • Jacquez, G.M.1
  • 109
    • 65249117580 scopus 로고    scopus 로고
    • NIFTI: An Evolutionary Approach for Finding Number of Clusters in Microarray Data
    • Jonnalagadda, S., Srinivasan, R. (2009). NIFTI: An Evolutionary Approach for Finding Number of Clusters in Microarray Data. BMC Bioinformatics, Vol. 10, p 40.
    • (2009) BMC Bioinformatics , vol.10 , pp. 40
    • Jonnalagadda, S.1    Srinivasan, R.2
  • 110
    • 4243128193 scopus 로고    scopus 로고
    • On clusterings: Good, bad and spectral
    • Kannan, R., Vempala, S., Vetta, A. (2004). On clusterings: Good, bad and spectral. Journal of the ACM, 51 (3), 497-515.
    • (2004) Journal of the ACM , vol.51 , Issue.3 , pp. 497-515
    • Kannan, R.1    Vempala, S.2    Vetta, A.3
  • 113
    • 0035979259 scopus 로고    scopus 로고
    • Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments
    • Kerr, M.K., Churchill, G.A. (2001). Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments. PNAS, Vol. 98 (16), 8961-8965.
    • (2001) PNAS. , vol.98 , Issue.16 , pp. 8961-8965
    • Kerr, M.K.1    Churchill, G.A.2
  • 116
    • 33746257145 scopus 로고    scopus 로고
    • The practice of cluster analysis
    • Kettenring, J.R. (2006). The practice of cluster analysis, J. Classif., 23, 3-30.
    • (2006) J. Classif. , vol.23 , pp. 3-30
    • Kettenring, J.R.1
  • 118
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for Feature Subset Selection
    • Kohavi, R, John, G.H. (1998). Wrappers for Feature Subset Selection. Artificial Intelligence, Vol. 97 (1-2), 273-324.
    • (1998) Artificial Intelligence. , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 120
    • 0020068152 scopus 로고
    • Self-organized formation of topologically correct feature maps
    • Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.
    • (1982) Biological Cybernetics , vol.43 , pp. 59-69
    • Kohonen, T.1
  • 122
    • 61849125822 scopus 로고    scopus 로고
    • A simple method for screening variables before clustering microarray data
    • Krzanowski, W.J., Hand, D.J. (2009). A simple method for screening variables before clustering microarray data. Computational Statistics and Data Analysis, Vol. 43, 2747-2753.
    • (2009) Computational Statistics and Data Analysis , vol.43 , pp. 2747-2753
    • Krzanowski, W.J.1    Hand, D.J.2
  • 123
    • 58149202361 scopus 로고    scopus 로고
    • Semi-Supervised Graph Clustering: A Kernel Approach
    • January 2009
    • Kulis, B., Basu, S., Dhillon, I.S., Mooney, R.J. (2009a). Semi-Supervised Graph Clustering: A Kernel Approach. Machine Learning, Vol. 74 (1), 1-22, January 2009.
    • (2009) Machine Learning. , vol.74 , Issue.1 , pp. 1-22
    • Kulis, B.1    Basu, S.2    Dhillon, I.S.3    Mooney, R.J.4
  • 126
    • 33750289836 scopus 로고    scopus 로고
    • A toolbox for k-centroids cluster analysis
    • Leisch, F. (2006). A toolbox for k-centroids cluster analysis. Comput. Stat. Data Anal., 51 (2), 526-544.
    • (2006) Comput. Stat. Data Anal. , vol.51 , Issue.2 , pp. 526-544
    • Leisch, F.1
  • 127
    • 77956881077 scopus 로고    scopus 로고
    • Visualizing cluster analysis and finite mixture models
    • In: Chen, C., Härdle, W., Unwin, A. (eds.) Springer Handbooks of Computational Statistics. Springer, Berlin (2008). ISBN 978-3-540-33036-3
    • Leisch, F. (2008). Visualizing cluster analysis and finite mixture models. In: Chen, C., Härdle, W., Unwin, A. (eds.) Handbook of Data Visualization. Springer Handbooks of Computational Statistics. Springer, Berlin (2008). ISBN 978-3-540-33036-3.
    • (2008) Handbook of Data Visualization.
    • Leisch, F.1
  • 128
    • 79961177930 scopus 로고    scopus 로고
    • Neighborhood graphs, stripes and shadow plots for cluster visualization
    • 2009. to appear
    • Leisch, F. (2009). Neighborhood graphs, stripes and shadow plots for cluster visualization. Statistics and Computing, 2009. to appear.
    • (2009) Statistics and Computing
    • Leisch, F.1
  • 130
    • 69849086336 scopus 로고    scopus 로고
    • QUBIC: A qualitative biclustering algorithm for analyses of gene expression data
    • August 1, 2009
    • Li, G., Ma, Q., Tang, H., Paterson, A. H., Xu, Y. (2009). QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Res., August 1, 2009; 37(15): e101-e101.
    • (2009) Nucleic Acids Res , vol.37 , Issue.15
    • Li, G.1    Ma, Q.2    Tang, H.3    Paterson, A.H.4    Xu, Y.5
  • 131
    • 0345040873 scopus 로고    scopus 로고
    • Classification and Regression by randomForest
    • Liaw, A., Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22. URL http://CRAN.R-project.org/doc/Rnews/.
    • (2002) R News , vol.2 , Issue.3 , pp. 18-22
    • Liaw, A.1    Wiener, M.2
  • 133
    • 17044405923 scopus 로고    scopus 로고
    • Towards integrating feature selection algorithms for classification and clustering
    • Liu, H., Yu, L. (2005). Towards integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17 (3), 1-12.
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.3 , pp. 1-12
    • Liu, H.1    Yu, L.2
  • 134
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • Luxburg, U. von. (2007). A tutorial on spectral clustering. Statistics and Computing, 17 (4), 395-416.
    • (2007) Statistics and Computing , vol.17 , Issue.4 , pp. 395-416
    • von Luxburg, U.1
  • 135
    • 77951840646 scopus 로고    scopus 로고
    • Clustering stability: An overview
    • URL (30-08-2010)
    • Luxburg, U. von. (2010). Clustering stability: an overview. Foundations and Trends in Machine Learning, Vol. 2 (3), 235-274, URL (30-08-2010): http://arxiv.org/abs/1007.1075.
    • (2010) Foundations and Trends in Machine Learning. , vol.2 , Issue.3 , pp. 235-274
    • von Luxburg, U.1
  • 139
    • 58849086813 scopus 로고    scopus 로고
    • CUR matrix decompositions for improved data analysis
    • January 20, 2009
    • Mahoney, M.W., Drineas, P. (2009). CUR matrix decompositions for improved data analysis. PNAS January 20, 2009, Vol. 106 (3), 697-702.
    • (2009) PNAS , vol.106 , Issue.3 , pp. 697-702
    • Mahoney, M.W.1    Drineas, P.2
  • 140
    • 0035619721 scopus 로고    scopus 로고
    • Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software
    • Makarenkov, V., Legendre, P. (2001). Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software. Journal of Classification, 18, 245-271.
    • (2001) Journal of Classification , vol.18 , pp. 245-271
    • Makarenkov, V.1    Legendre, P.2
  • 141
    • 0141985590 scopus 로고    scopus 로고
    • Area-based tests for association between spatial patterns
    • Maruca, S.L., Jacquez, G.M. (2002). Area-based tests for association between spatial patterns. Journal of Geographic Systems, 4 (1), 69-83.
    • (2002) Journal of Geographic Systems , vol.4 , Issue.1 , pp. 69-83
    • Maruca, S.L.1    Jacquez, G.M.2
  • 144
    • 0001179368 scopus 로고
    • Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data
    • McQuitty, L.L. (1966). Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825-831.
    • (1966) Educational and Psychological Measurement , vol.26 , pp. 825-831
    • McQuitty, L.L.1
  • 145
    • 33947156744 scopus 로고    scopus 로고
    • Comparing clusterings-an information based distance
    • Meila, M. (2007). Comparing clusterings-an information based distance. Journal of Multivariate Analysis, 98, 873-895.
    • (2007) Journal of Multivariate Analysis , vol.98 , pp. 873-895
    • Meila, M.1
  • 146
    • 79960940517 scopus 로고    scopus 로고
    • Finite mixture models and model-based clustering
    • Melnykov, V. Maitra, R. (2010). Finite mixture models and model-based clustering. Statistics Surveys, 2010, Vol. 4, 80-116.
    • (2010) Statistics Surveys, 2010 , vol.4 , pp. 80-116
    • Melnykov, V.1    Maitra, R.2
  • 147
    • 33847457966 scopus 로고
    • An examination of the effect of six types of error perturbation on fifteen clustering algorithms
    • Milligan, G.W. (1980). An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45, 325-342.
    • (1980) Psychometrika , vol.45 , pp. 325-342
    • Milligan, G.W.1
  • 148
    • 34250115918 scopus 로고
    • An examination of procedures for determining the number of clusters in a data set
    • Milligan, G.W., Cooper, M.C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 159-179.
    • (1985) Psychometrika , vol.50 , pp. 159-179
    • Milligan, G.W.1    Cooper, M.C.2
  • 150
    • 84989454185 scopus 로고
    • A note on procedures for testing the quality of a clustering of a set of objects
    • Milligan, G.W., Mahajan, V. (1980). A note on procedures for testing the quality of a clustering of a set of objects. Decision Sciences, 11, 669-677.
    • (1980) Decision Sciences , vol.11 , pp. 669-677
    • Milligan, G.W.1    Mahajan, V.2
  • 151
    • 0002048998 scopus 로고
    • A validation study of a variable weighting algorithm for cluster analysis
    • Milligan, G.W. (1989). A validation study of a variable weighting algorithm for cluster analysis. Journal of Classification, 6 (1), 53-71.
    • (1989) Journal of Classification , vol.6 , Issue.1 , pp. 53-71
    • Milligan, G.W.1
  • 152
    • 0002271592 scopus 로고    scopus 로고
    • Clustering validation: Results and implications for applied analyses
    • In P. Arabie, L. J. Hubert, and G. D. Soete, editors, In World Scientic Publishing, River Edge, NJ, 1996
    • Milligan, G.W. (1996). Clustering validation: results and implications for applied analyses. In P. Arabie, L. J. Hubert, and G. D. Soete, editors, In Clustering and Classication., pages 341-375. World Scientic Publishing, River Edge, NJ, 1996.
    • (1996) Clustering and Classication , pp. 341-375
    • Milligan, G.W.1
  • 153
    • 33746829948 scopus 로고    scopus 로고
    • Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms
    • Mingoti, S.A., Lima, J.O. (2006). Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research, 174, 1742-1759.
    • (2006) European Journal of Operational Research , vol.174 , pp. 1742-1759
    • Mingoti, S.A.1    Lima, J.O.2
  • 157
    • 0038724494 scopus 로고    scopus 로고
    • Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
    • Monti, S., Tamayo, P., Mesirov, J., Golub, T. (2003). Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn., 52, 91-118.
    • (2003) Mach. Learn. , vol.52 , pp. 91-118
    • Monti, S.1    Tamayo, P.2    Mesirov, J.3    Golub, T.4
  • 159
    • 4243465365 scopus 로고
    • Non-uniqueness and inversions in cluster analysis
    • Morgan, B.J.T., Ray, A.P.G. (1995). Non-uniqueness and inversions in cluster analysis. Applied Statistics, 44, 117-34.
    • (1995) Applied Statistics , vol.44 , pp. 117-134
    • Morgan, B.J.T.1    Ray, A.P.G.2
  • 160
    • 32944458442 scopus 로고    scopus 로고
    • A connectionist and multivariate approach to science maps: The SOM, clustering and MDS applied to library and information science research
    • 2006
    • Moya-Anegón, F., Herrero-Solana, V., Jiménez-Contreras, E. (2006). A connectionist and multivariate approach to science maps: the SOM, clustering and MDS applied to library and information science research Journal of Information Science, 32 (1) 2006, 63-77.
    • (2006) Journal of Information Science , vol.32 , Issue.1 , pp. 63-77
    • Moya-Anegón, F.1    Herrero-Solana, V.2    Jiménez-Contreras, E.3
  • 161
    • 21844508468 scopus 로고
    • The Kohonen self-organizing map method: An assessment
    • Murtagh, F., Hernández-Pajares, M. (1995). The Kohonen self-organizing map method: An assessment. Journal of Classification. Vol. 12 (2), 165-190.
    • (1995) Journal of Classification , vol.12 , Issue.2 , pp. 165-190
    • Murtagh, F.1    Hernández-Pajares, M.2
  • 163
    • 53349128921 scopus 로고    scopus 로고
    • Detecting spatial hot spots in landscape ecology
    • Nelson, T.A., Boots, B. (2008). Detecting spatial hot spots in landscape ecology. Ecography, Vol. 31 (5), 556-566.
    • (2008) Ecography. , vol.31 , Issue.5 , pp. 556-566
    • Nelson, T.A.1    Boots, B.2
  • 164
    • 0038718854 scopus 로고    scopus 로고
    • The structure and function of complex networks
    • 2003-JSTOR
    • Newman, M.E.J. (2003). The structure and function of complex networks. SIAM review, 2003-JSTOR.
    • (2003) SIAM review
    • Newman, M.E.J.1
  • 165
    • 42749100809 scopus 로고    scopus 로고
    • Fast algorithm for detecting community structure in networks
    • Newman, M.E.J. (2004). Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133.
    • (2004) Phys. Rev. E , vol.69 , pp. 066133
    • Newman, M.E.J.1
  • 166
    • 34547405111 scopus 로고    scopus 로고
    • Mixture models and exploratory analysis in networks
    • Newman, M.E.J., Leicht, E.A. (2007). Mixture models and exploratory analysis in networks. PNAS, Vol. 104 (23), 9564-9569.
    • (2007) PNAS. , vol.104 , Issue.23 , pp. 9564-9569
    • Newman, M.E.J.1    Leicht, E.A.2
  • 170
    • 0035740890 scopus 로고    scopus 로고
    • Testing for local spatial autocorrelation in the presence of global autocorrelation
    • Ord, J.K., Getis, A. (2001). Testing for local spatial autocorrelation in the presence of global autocorrelation. Journal of Regional Science, Vol. 41 (3), 411-432.
    • (2001) Journal of Regional Science. , vol.41 , Issue.3 , pp. 411-432
    • Ord, J.K.1    Getis, A.2
  • 171
    • 20444504323 scopus 로고    scopus 로고
    • Uncovering the overlapping community structure of complex networks in nature and society
    • Palla, G., Derényi, I., Farkas, I., Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 814-818.
    • (2005) Nature , vol.435 , pp. 814-818
    • Palla, G.1    Derényi, I.2    Farkas, I.3    Vicsek, T.4
  • 173
    • 0032673421 scopus 로고    scopus 로고
    • Displaying a clustering with CLUSPLOT
    • Pison, G., Struyf, A., Rousseeuw, P.J. (1999). Displaying a clustering with CLUSPLOT. Comput. Stat. Data Anal., 30, 381-392 http://ftp.win.ua.ac.be/pub/preprints/99/Disclu99.pdf.
    • (1999) Comput. Stat. Data Anal. , vol.30 , pp. 381-392
    • Pison, G.1    Struyf, A.2    Rousseeuw, P.J.3
  • 174
    • 3042781262 scopus 로고    scopus 로고
    • Local spatial autocorrelation statistics quantify multi=scale patterns in distributional data: An example from the Maya Lowlands
    • Premo, L.S. (2004). Local spatial autocorrelation statistics quantify multi=scale patterns in distributional data: an example from the Maya Lowlands. Journal of Archaeological Science, Vol. 31, 855-866.
    • (2004) Journal of Archaeological Science , vol.31 , pp. 855-866
    • Premo, L.S.1
  • 176
    • 0002490026 scopus 로고    scopus 로고
    • Data cleaning: Problems and current approaches
    • Rahm, E., Do, H.H. (2000). Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin, 23 (4), 3-13.
    • (2000) IEEE Data Engineering Bulletin , vol.23 , Issue.4 , pp. 3-13
    • Rahm, E.1    Do, H.H.2
  • 178
    • 0023453329 scopus 로고
    • Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
    • Rousseeuw, P.J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math, 20, 53-65.
    • (1987) J. Comput. Appl. Math , vol.20 , pp. 53-65
    • Rousseeuw, P.J.1
  • 179
    • 0033481728 scopus 로고    scopus 로고
    • The bagplot: A bivariate boxplot
    • Rousseeuw, P.J., Ruts, I., Tukey, J.W. (1999). The bagplot: A bivariate boxplot. Am. Stat., 53 (4), 382-387.
    • (1999) Am. Stat. , vol.53 , Issue.4 , pp. 382-387
    • Rousseeuw, P.J.1    Ruts, I.2    Tukey, J.W.3
  • 182
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys, Y., Inza, I., Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics. Vol. 23 (19), 2507-17.
    • (2007) Bioinformatics , vol.23 , Issue.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larrañaga, P.3
  • 183
    • 37249017479 scopus 로고    scopus 로고
    • A Bounded Index for Cluster Validity
    • In: P. Perner (Ed.), LNAI 4571, Springer Verlag, Heidelberg, 2007
    • Saitta, S., Raphael, B., Smith, I.F.C. (2007). A Bounded Index for Cluster Validity. In: P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, LNAI 4571, Springer Verlag, Heidelberg, pp. 174-187, 2007.
    • (2007) Machine Learning and Data Mining in Pattern Recognition , pp. 174-187
    • Saitta, S.1    Raphael, B.2    Smith, I.F.C.3
  • 185
    • 16244377091 scopus 로고    scopus 로고
    • Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
    • 2004
    • Salvador, S., Chan, P. (2004). Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. Proc. 16th IEEE Intl. Conf. on Tools with AI, 576-584, 2004.
    • (2004) Proc. 16th IEEE Intl. Conf. on Tools with AI , pp. 576-584
    • Salvador, S.1    Chan, P.2
  • 187
    • 64549114102 scopus 로고    scopus 로고
    • gcExplorer: Interactive Exploration of Gene Clusters
    • Scharl, T., Leisch, F. (2009). gcExplorer: Interactive Exploration of Gene Clusters. Bioinformatics, Vol. 25 (8), 1089-1090.
    • (2009) Bioinformatics. , vol.25 , Issue.8 , pp. 1089-1090
    • Scharl, T.1    Leisch, F.2
  • 188
    • 0002570938 scopus 로고    scopus 로고
    • Kernel Principal Component Analysis
    • In: Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola (Eds.), 1999, MIT Press Cambridge, MA, USA, ISBN 0-262-19416-3
    • Schölkopf, B., Smola, A., Müller, K.-R. (1999). Kernel Principal Component Analysis, In: Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola (Eds.), Advances in Kernel Methods-Support Vector Learning, 1999, MIT Press Cambridge, MA, USA, 327-352. ISBN 0-262-19416-3.
    • (1999) Advances in Kernel Methods-Support Vector Learning , pp. 327-352
    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3
  • 190
    • 85129748705 scopus 로고    scopus 로고
    • The clustergram: A graph for visualizing hierarchical and nonhierarchical cluster analyses
    • Schonlau, M. (2002). The clustergram: a graph for visualizing hierarchical and nonhierarchical cluster analyses. The Stata Journal, 2002, 2 (4), 391-402.
    • (2002) The Stata Journal, 2002 , vol.2 , Issue.4 , pp. 391-402
    • Schonlau, M.1
  • 191
    • 1542403907 scopus 로고    scopus 로고
    • Visualizing Hierarchical and Non-Hierarchical Cluster Analyses with Clustergrams
    • Schonlau, M. (2004). Visualizing Hierarchical and Non-Hierarchical Cluster Analyses with Clustergrams. Computational Statistics: 2004, 19 (1), 95-111.
    • (2004) Computational Statistics: 2004 , vol.19 , Issue.1 , pp. 95-111
    • Schonlau, M.1
  • 192
    • 0034244751 scopus 로고    scopus 로고
    • Normalized cuts and image segmentation
    • Shi, J., Malik, J. (2000). Normalized cuts and image segmentation. IEEE Trans. PAMI, 22 (8), 888-905.
    • (2000) IEEE Trans. PAMI , vol.22 , Issue.8 , pp. 888-905
    • Shi, J.1    Malik, J.2
  • 193
    • 16444381830 scopus 로고    scopus 로고
    • Tumor classification by tissue microarray profiling: Random forest clustering applied to renal cell carcinoma
    • Shi, T., et al. (2005). Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma. Modern Pathology., 18, 547-557.
    • (2005) Modern Pathology. , vol.18 , pp. 547-557
    • Shi, T.1
  • 194
  • 195
    • 79956257940 scopus 로고    scopus 로고
    • Categorisation of typical vulnerability patterns in global drylands
    • Sietz, D., Lüdeke, M.K.B., Walther, C. (2011). Categorisation of typical vulnerability patterns in global drylands. Global Environmental Change, 21, 431-440.
    • (2011) Global Environmental Change , vol.21 , pp. 431-440
    • Sietz, D.1    Lüdeke, M.K.B.2    Walther, C.3
  • 196
    • 0040220513 scopus 로고
    • Scales of Measurement and Cluster Analysis: An Application Concerning Market Segments in the Babyfood market
    • Silver, M. (1995). Scales of Measurement and Cluster Analysis: An Application Concerning Market Segments in the Babyfood market. The Statistician, Vol. 44 (1), 101-112.
    • (1995) The Statistician. , vol.44 , Issue.1 , pp. 101-112
    • Silver, M.1
  • 198
    • 29144480705 scopus 로고    scopus 로고
    • Clustering noisy data in a reduced dimension space via multivariate regression trees
    • Smyth, C.W., Coomans, D.H., Everingham, Y.L. (2006a). Clustering noisy data in a reduced dimension space via multivariate regression trees. Pattern Recognition, Vol. 39, 424-431.
    • (2006) Pattern Recognition , vol.39 , pp. 424-431
    • Smyth, C.W.1    Coomans, D.H.2    Everingham, Y.L.3
  • 200
    • 35648970146 scopus 로고    scopus 로고
    • Predictice weighting for cluster ensembles
    • Smyth, C.W., Coomans, D.H. (2007). Predictice weighting for cluster ensembles. Journal of Chemometrics, Vol. 21, 364-375.
    • (2007) Journal of Chemometrics , vol.21 , pp. 364-375
    • Smyth, C.W.1    Coomans, D.H.2
  • 201
    • 30544446988 scopus 로고    scopus 로고
    • Instability of hierarchical cluster analysis due to input order of the data: The PermuCLUSTER solution
    • Spaans, M., Heiser, W.J. (2005). Instability of hierarchical cluster analysis due to input order of the data: The PermuCLUSTER solution. Psychological Methods, 10 (4), 468-476.
    • (2005) Psychological Methods , vol.10 , Issue.4 , pp. 468-476
    • Spaans, M.1    Heiser, W.J.2
  • 202
    • 4344611435 scopus 로고    scopus 로고
    • Properties of the Hubert-Arabie adjusted Rand index
    • Steinley, D. (2004). Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9, 386-396.
    • (2004) Psychological Methods , vol.9 , pp. 386-396
    • Steinley, D.1
  • 205
    • 34250871625 scopus 로고    scopus 로고
    • Initializing K-means batch clustering: A critical evaluation of several techniques
    • Steinley, D., Brusco, M.J. (2007). Initializing K-means batch clustering: A critical evaluation of several techniques. Journal of Classification, 24, 99-121.
    • (2007) Journal of Classification , vol.24 , pp. 99-121
    • Steinley, D.1    Brusco, M.J.2
  • 206
    • 41149180750 scopus 로고    scopus 로고
    • A new variable weighting and selection procedure for K-means cluster analysis
    • Steinley, D., Brusco, M.J. (2008a). A new variable weighting and selection procedure for K-means cluster analysis. Multivariate Behavioral Research, Vol. 43, 77-108.
    • (2008) Multivariate Behavioral Research , vol.43 , pp. 77-108
    • Steinley, D.1    Brusco, M.J.2
  • 207
    • 41449108683 scopus 로고    scopus 로고
    • Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures
    • Steinley, D., Brusco, M.J. (2008b). Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures. Psychometrika, Vol. 73 (1), 125-144.
    • (2008) Psychometrika. , vol.73 , Issue.1 , pp. 125-144
    • Steinley, D.1    Brusco, M.J.2
  • 208
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles-a knowledge reuse framework for combining multiple partitions
    • Strehl, A., Ghosh, J. (2002). Cluster ensembles-a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583-617.
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 210
    • 33847096395 scopus 로고    scopus 로고
    • Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution
    • Strobl, C., Boulesteix, A.L., Zeileis, A., Hothorn, T. (2007). Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics 2007, 8, 25.
    • (2007) BMC Bioinformatics 2007 , vol.8 , pp. 25
    • Strobl, C.1    Boulesteix, A.L.2    Zeileis, A.3    Hothorn, T.4
  • 212
    • 79953208337 scopus 로고    scopus 로고
    • An Improved Clustering Algorithm Based on Density Distribution Function
    • August 2010, URL (31-08-2010)
    • Tan, J., Zhang, J., Li, W. (2010). An Improved Clustering Algorithm Based on Density Distribution Function. Computer and Information Science, Vol. 3 (3), August 2010, 23-29. URL (31-08-2010): http://ccsenet.org/journal/index.php/cis/article/viewFile/6891/5426.
    • (2010) Computer and Information Science. , vol.3 , Issue.3 , pp. 23-29
    • Tan, J.1    Zhang, J.2    Li, W.3
  • 213
    • 11244306358 scopus 로고    scopus 로고
    • Discovering statistically significant biclusters in gene expression data
    • Tanay, R., Sharan, R., Shamir, R. (2002). Discovering statistically significant biclusters in gene expression data. Bioinformatics Vol. 18 (9), S136-S144.
    • (2002) Bioinformatics , vol.18 , Issue.9
    • Tanay, R.1    Sharan, R.2    Shamir, R.3
  • 214
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum, J.B., de Silva, V., Langford, J.C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, Vol. 290, 2319-2323.
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.B.1    de Silva, V.2    Langford, J.C.3
  • 215
    • 84866370391 scopus 로고    scopus 로고
    • A Multivariate Adaptive Stochastic Search Method for Dimensionality Reduction in Classification
    • Tian, T., James, G., Wilcox, R. (2009). A Multivariate Adaptive Stochastic Search Method for Dimensionality Reduction in Classification. Annals of Applied Statistics, 4, 339-364.
    • (2009) Annals of Applied Statistics , vol.4 , pp. 339-364
    • Tian, T.1    James, G.2    Wilcox, R.3
  • 218
    • 44349127297 scopus 로고    scopus 로고
    • Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
    • Tsai, C.Y., Chiu, C.C. (2008). Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm. Computational Statistics & Data Analysis, Vol. 52 (10), 4658-4672.
    • (2008) Computational Statistics & Data Analysis , vol.52 , Issue.10 , pp. 4658-4672
    • Tsai, C.Y.1    Chiu, C.C.2
  • 219
    • 33645861937 scopus 로고    scopus 로고
    • Statistical Inference for Variable Importance
    • van der Laan, M. (2006). Statistical Inference for Variable Importance. International Journal of Biostatistics, 2 (1), 1008-1008.
    • (2006) International Journal of Biostatistics , vol.2 , Issue.1 , pp. 1008
    • van der Laan, M.1
  • 220
    • 33747883187 scopus 로고    scopus 로고
    • Novel unsupervised feature filtering of biological data
    • Varshavsky, R., Gottlieb, A., Linial, M., Horn, D. (2006). Novel unsupervised feature filtering of biological data. Bioinformatics, Vol. 22, e507-e513.
    • (2006) Bioinformatics , vol.22
    • Varshavsky, R.1    Gottlieb, A.2    Linial, M.3    Horn, D.4
  • 221
    • 36949015566 scopus 로고    scopus 로고
    • Unsupervised feature selection under perturbations: Meeting the challenges of biological data
    • Varshavsky, R., Gottlieb, A., Horn, D., Linial, M. (2007). Unsupervised feature selection under perturbations: meeting the challenges of biological data. Bioinformatics, Vol. 23 (24), 3343-3349.
    • (2007) Bioinformatics. , vol.23 , Issue.24 , pp. 3343-3349
    • Varshavsky, R.1    Gottlieb, A.2    Horn, D.3    Linial, M.4
  • 222
    • 38049168357 scopus 로고    scopus 로고
    • SOM-based data visualization methods
    • Vesanto, J. (1999). SOM-based data visualization methods, Intelligent Data Analysis, 3 (2), 111-126.
    • (1999) Intelligent Data Analysis , vol.3 , Issue.2 , pp. 111-126
    • Vesanto, J.1
  • 225
    • 17044407257 scopus 로고    scopus 로고
    • Feature Selection in Microarray Analysis
    • in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), Kluwer Academic Publishers, 2003
    • Xing, E.P. (2003). Feature Selection in Microarray Analysis, in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, 2003.
    • (2003) A Practical Approach to Microarray Data Analysis
    • Xing, E.P.1
  • 228
    • 0035024021 scopus 로고    scopus 로고
    • Validating clustering for analysis for clustering gene expression data
    • Yeung, K.Y., Haynor, D.R., Ruzzo, W.L. (2001). Validating clustering for analysis for clustering gene expression data. Bioinformatics, Vol. 17 (4), 309-318.
    • (2001) Bioinformatics. , vol.17 , Issue.4 , pp. 309-318
    • Yeung, K.Y.1    Haynor, D.R.2    Ruzzo, W.L.3
  • 229
    • 0034800371 scopus 로고    scopus 로고
    • Principal component analysis for clustering gene expression data
    • Yeung, K.Y., Ruzzo, W.L. (2001). Principal component analysis for clustering gene expression data. Bioinformatics, Vol. 17 (9), 763-774.
    • (2001) Bioinformatics. , vol.17 , Issue.9 , pp. 763-774
    • Yeung, K.Y.1    Ruzzo, W.L.2
  • 231
    • 85130909127 scopus 로고    scopus 로고
    • Feature Selection for Genomic Data Analysis
    • In H. Liu, editor, Chapman and Hall/CRC Press, 2007
    • Yu, L. (2007). Feature Selection for Genomic Data Analysis. In H. Liu, editor, Computational Methods for Feature Selection, Chapman and Hall/CRC Press, 2007.
    • (2007) Computational Methods for Feature Selection
    • Yu, L.1
  • 234
    • 33947301110 scopus 로고    scopus 로고
    • Support Vector Machine Implementations for Classification & Clustering
    • doi:10.1186/1471-2105-7-S2-S4
    • Winters-Hilt, S., Yelundur, A., McChesney, C., Landry, M. (2006). Support Vector Machine Implementations for Classification & Clustering. BMC Bioinformatics 2006, 7(Suppl 2):S4 doi:10.1186/1471-2105-7-S2-S4.
    • (2006) BMC Bioinformatics 2006 , vol.7 , Issue.SUPPL. 2
    • Winters-Hilt, S.1    Yelundur, A.2    McChesney, C.3    Landry, M.4
  • 235
    • 38549155115 scopus 로고    scopus 로고
    • SVM clustering
    • doi:10.1186/1471-2105-8-S7-S18
    • Winters-Hilt, S., Merat, S. (2007). SVM clustering. BMC Bioinformatics 2007, 8 (Suppl 7):S18 doi:10.1186/1471-2105-8-S7-S18.
    • (2007) BMC Bioinformatics 2007 , vol.8 , Issue.SUPPL. 7
    • Winters-Hilt, S.1    Merat, S.2
  • 236
    • 23144467675 scopus 로고    scopus 로고
    • GEMS: A web server for biclustering analysis of expression data
    • Web Server Issue
    • Wu, C.-J., Kasif, S. (2005). GEMS: a web server for biclustering analysis of expression data. Nucleic Acids Research 2005 33(Web Server Issue):W596-W599.
    • (2005) Nucleic Acids Research 2005 , vol.33
    • Wu, C.-J.1    Kasif, S.2
  • 237
    • 18444403761 scopus 로고    scopus 로고
    • A cluster validity index for fuzzy clustering
    • Wu, K.L., Yang, M.S. (2005). A cluster validity index for fuzzy clustering, Pattern Recognition Lett., Vol. 26, 1275-1291.
    • (2005) Pattern Recognition Lett. , vol.26 , pp. 1275-1291
    • Wu, K.L.1    Yang, M.S.2
  • 238
    • 67649600665 scopus 로고    scopus 로고
    • Robust cluster validity indexes
    • Wu, K.L., Yang, M.S., Hsieh, J.N. (2009). Robust cluster validity indexes. Pattern Recognition. Vol. 42 (11), 2541-2550.
    • (2009) Pattern Recognition , vol.42 , Issue.11 , pp. 2541-2550
    • Wu, K.L.1    Yang, M.S.2    Hsieh, J.N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.