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Volumn 18, Issue , 2002, Pages 1-40

Clustering methods and their uses in computational chemistry

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EID: 33645265985     PISSN: 10693599     EISSN: None     Source Type: Book Series    
DOI: 10.1002/0471433519.ch1     Document Type: Article
Times cited : (208)

References (145)
  • 3
  • 4
    • 0004241258 scopus 로고    scopus 로고
    • 2nd ed., Chapman and Hall, London
    • A. D. Gordon, Classification, 2nd ed., Chapman and Hall, London, 1999.
    • (1999) Classification
    • Gordon, A.D.1
  • 7
    • 33751392117 scopus 로고
    • Clustering of chemical structures on the basis of two-dimensional similarity measures
    • J. M. Barnard and G. M. Downs, J. Chem. Inf. Comput. Sci., 32 (6), 644 (1992). Clustering of Chemical Structures on the Basis of Two-Dimensional Similarity Measures.
    • (1992) J. Chem. Inf. Comput. Sci. , vol.32 , Issue.6 , pp. 644
    • Barnard, J.M.1    Downs, G.M.2
  • 8
    • 0001526783 scopus 로고
    • H. van de Waterbeernd, Ed., VCH Publishers, Weinheim. Clustering of Chemical Structure Databases for Compound Selection
    • G. M. Downs and P. Willett, in Advanced Computer-Assisted Techniques in Drug Discovery, H. van de Waterbeernd, Ed., VCH Publishers, Weinheim, 1994, pp. 111-130. Clustering of Chemical Structure Databases for Compound Selection.
    • (1994) Advanced Computer-Assisted Techniques in Drug Discovery , pp. 111-130
    • Downs, G.M.1    Willett, P.2
  • 9
    • 0034355922 scopus 로고    scopus 로고
    • Computer-aided molecular diversity analysis and combinatorial library design
    • K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York
    • R. A. Lewis, S. D. Pickett, and D. E. Clark, in Revieivs in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 2000, Vol.16, pp. 1-51. Computer-Aided Molecular Diversity Analysis and Combinatorial Library Design.
    • (2000) Revieivs in Computational Chemistry , vol.16 , pp. 1-51
    • Lewis, R.A.1    Pickett, S.D.2    Clark, D.E.3
  • 10
    • 77949946184 scopus 로고    scopus 로고
    • Clustan Ltd. 16 Kingsburgh Road, Edinburgh, UK
    • Clustan Ltd., 16 Kingsburgh Road, Edinburgh, UK. http://www.clustan.com.
  • 11
    • 77949969393 scopus 로고    scopus 로고
    • SAS Institute Inc., SAS Campus Drive, Cary, NC 27513, USA
    • SAS Institute Inc., SAS Campus Drive, Cary, NC 27513, USA. http://www.sas.com.
  • 12
    • 77950005705 scopus 로고    scopus 로고
    • Barnard Chemical Information Ltd., 46 Uppergate Road, Stannington, Sheffield S6 6BX, UK
    • Barnard Chemical Information Ltd., 46 Uppergate Road, Stannington, Sheffield S6 6BX, UK. http://wivw.bci.gb.com.
  • 13
    • 77949953464 scopus 로고    scopus 로고
    • Chemical Computing Group Inc. 1010 Sherbrooke Street West Suite 910 Montreal Quebec H3A 2R7 Canada
    • Chemical Computing Group Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Quebec H3A 2R7, Canada, http://www.chemcomp.com.
  • 14
    • 77949988518 scopus 로고    scopus 로고
    • Daylight Chemical Information Systems Inc., 441 Greg Avenue, Santa Fe, NM 87501, USA
    • Daylight Chemical Information Systems Inc., 441 Greg Avenue, Santa Fe, NM 87501, USA. http://www.daylight.com.
  • 15
    • 77950013287 scopus 로고    scopus 로고
    • Daylight Chemical Information Systems Inc., 441 Greg Avenue, Santa Fe, NM 87501, USA
    • Daylight Chemical Information Systems Inc., 441 Greg Avenue, Santa Fe, NM 87501, USA. http://www.daylight.com.
  • 16
    • 77949957606 scopus 로고    scopus 로고
    • MDL Information Systems Inc., 14600 Catalina Street, San Leandro, CA 94577, USA
    • MDL Information Systems, Inc., 14600 Catalina Street, San Leandro, CA 94577, USA. http://www.mdl.com.
  • 17
    • 77949998386 scopus 로고    scopus 로고
    • Accelrys (formerly Molecular Simulations Inc.), 9685 Scranton Road, San Diego, CA 92121-93752, USA
    • Accelrys (formerly Molecular Simulations Inc.), 9685 Scranton Road, San Diego, CA 92121-93752, USA. http://www.accelrys.com.
  • 18
    • 77949937481 scopus 로고    scopus 로고
    • Tripos Inc., 1699 South Hanley Road, St. Louis, MO 63144, USA
    • Tripos Inc., 1699 South Hanley Road, St. Louis, MO 63144, USA. http://www.tripos.com.
  • 19
    • 0006590019 scopus 로고    scopus 로고
    • P. Willett, Ed., Perspectives in Drug Discovery and Design, Kluwer/ESCOM, Dordrecht, The Netherlands, Commercial Software Systems for Diversity Analysis
    • W. A. Warr, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol.7/8, Kluwer/ESCOM, Dordrecht, The Netherlands, 1997, pp. 115-130. Commercial Software Systems for Diversity Analysis.
    • (1997) Computational Methods for the Analysis of Molecular Diversity , vol.7-8 , pp. 115-130
    • Warr, W.A.1
  • 20
    • 0002100872 scopus 로고    scopus 로고
    • P. Willett, Ed., Perspectives in Drug Discovery and Design, Kluwer/ESCOM, Dordrecht, The Netherlands, Descriptors for Diversity Analysis
    • R. D. Brown, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol.7/8, Kluwer/ESCOM, Dordrecht, The Netherlands, 1997, pp. 31-49. Descriptors for Diversity Analysis.
    • (1997) Computational Methods for the Analysis of Molecular Diversity , vol.7-8 , pp. 31-49
    • Brown, R.D.1
  • 21
    • 0342645323 scopus 로고    scopus 로고
    • Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection
    • R. D. Brown and Y. C. Martin, J. Chem. Inf. Comput. Sci., 36 (3), 572 (1996). Use of Structure-Activity Data to Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection.
    • (1996) J. Chem. Inf. Comput. Sci. , vol.36 , Issue.3 , pp. 572
    • Brown, R.D.1    Martin, Y.C.2
  • 22
    • 5244364312 scopus 로고    scopus 로고
    • The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding
    • R. D. Brown and Y. C. Martin, J. Chem. Inf. Comput. Sci., 37 (1), 1 (1997). The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding.
    • (1997) J. Chem. Inf. Comput. Sci. , vol.37 , Issue.1 , pp. 1
    • Brown, R.D.1    Martin, Y.C.2
  • 23
    • 0030534377 scopus 로고
    • Similarity searching in databases of chemical structures
    • K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York
    • G. M. Downs and P. Willett, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1995, Vol.7, pp. 1-66. Similarity Searching in Databases of Chemical Structures.
    • (1995) Reviews in Computational Chemistry , vol.7 , pp. 1-66
    • Downs, G.M.1    Willett, P.2
  • 25
    • 84956824135 scopus 로고    scopus 로고
    • Descriptor-based similarity measures for screening chemical databases
    • H.-J. Bohm and G. Schneider., Eds., Wiley, New York
    • J. M. Barnard, G. M. Downs, and P. Willett, in Virtual Screening for Bioactive Molecules, H.-J. Bohm and G. Schneider., Eds., Wiley, New York, 2000, pp. 59-80. Descriptor-Based Similarity Measures for Screening Chemical Databases.
    • (2000) Virtual Screening for Bioactive Molecules , pp. 59-80
    • Barnard, J.M.1    Downs, G.M.2    Willett, P.3
  • 26
    • 0002556215 scopus 로고    scopus 로고
    • Partition-based selection
    • P. Willett, Ed., Perspectives in Drug Discovery and Design, Kluwer/ ESCOM, Dordrecht, The Netherlands
    • J. S. Mason and S. D. Pickett, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol.7/8, Kluwer/ ESCOM, Dordrecht, The Netherlands, 1997, pp. 85-114. Partition-Based Selection.
    • (1997) Computational Methods for the Analysis of Molecular Diversity , vol.7-8 , pp. 85-114
    • Mason, J.S.1    Pickett, S.D.2
  • 28
    • 0142067924 scopus 로고
    • Multidimensional clustering algorithms
    • F. Murtagh, COMPSTAT Lectures, 4, (1985). Multidimensional Clustering Algorithms.
    • (1985) COMPSTAT Lectures , vol.4
    • Murtagh, F.1
  • 30
    • 0003126317 scopus 로고
    • A general theory of classificatory sorting strategies. 1. Hierarchical systems
    • G. N. Lance and W. T. Williams, Computer J., 9, 373 (1967). A General Theory of Classificatory Sorting Strategies. 1. Hierarchical Systems.
    • (1967) Computer J. , vol.9 , Issue.373
    • Lance, G.N.1    Williams, W.T.2
  • 31
    • 84944178665 scopus 로고
    • Hierarchical grouping to optimize an objective function
    • J. H. Ward, J. Am. Stat. Assoc, 58, 236 (1963). Hierarchical Grouping to Optimize an Objective Function.
    • (1963) J. Am. Stat. Assoc. , vol.58 , pp. 236
    • Ward, J.H.1
  • 32
    • 0022906994 scopus 로고
    • Implementing agglomerative hierarchic clustering algorithms for use in document retrieval
    • E. M. Voorhees, Inf. Processing Management, 22 (6), 465 (1986). Implementing Agglom-erative Hierarchic Clustering Algorithms for Use in Document Retrieval.
    • (1986) Processing Inf. Management , vol.22 , Issue.6 , pp. 465
    • Voorhees, E.M.1
  • 33
    • 0000128084 scopus 로고    scopus 로고
    • Recursive partitioning analysis of a large structure-activity data set using three-dimen-sional descriptors
    • X. Chen, A. Rusinko HI, and S. S. Young,J. Chem. Inf. Comput. Sci., 38 (6), 1054 (1998). Recursive Partitioning Analysis of a Large Structure-Activity Data Set Using Three-Dimen-sional Descriptors.
    • (1998) J. Chem. Inf. Comput. Sci. , vol.38 , Issue.6 , pp. 1054
    • Chen, X.1    Rusinko III, A.2    Young, S.S.3
  • 34
    • 0004173623 scopus 로고
    • Efficient algorithms for divisive hierarchical clustering with the diameter criterion
    • A. Guenoche, P. Hansen, and B. Jaumard, J. Classification, 8, 5 (1991). Efficient Algorithms for Divisive Hierarchical Clustering with the Diameter Criterion.
    • (1991) Classification J. , vol.8 , pp. 5
    • Guenoche, A.1    Hansen, P.2    Jaumard, B.3
  • 35
    • 0015680655 scopus 로고
    • Clustering using a similarity measure based on shared near neighbors
    • R. A. Jarvis and E. A. Patrick, IEEE Trans. Computers, C-22 (11), 1025 (1973). Clustering Using a Similarity Measure Based on Shared Near Neighbors.
    • (1973) IEEE Trans. Computers C-22 (11) , vol.1025
    • Jarvis, R.A.1    Patrick, E.A.2
  • 36
    • 0040972986 scopus 로고
    • A review of the use of inverted files for best match searching in information rretrieval systems
    • S. A. Perry and P. Willett, J. Inf. Sci., 6, 59 (1983). A Review of the Use of Inverted Files for Best Match Searching in Information Rretrieval Systems.
    • (1983) J. Inf. Sci. , vol.6 , pp. 59
    • Perry, S.A.1    Willett, P.2
  • 37
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm
    • A. P. Dempster, N. M. Laird, and D. B. Rubin, J. Royal Stat. Soc, B39, 1 (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm.
    • (1977) Royal J. Stat. Soc. , vol.B39 , pp. 1
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 40
    • 0034367139 scopus 로고    scopus 로고
    • Artificial neural networks and their use in chemistry
    • K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York
    • See also, K. L. Peterson, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 2000, Vol.16, pp. 53-140. Artificial Neural Networks and Their Use in Chemistry.
    • (2000) Reviews in Computational Chemistry , vol.16 , pp. 53-140
    • Peterson, K.L.1
  • 41
    • 0005983330 scopus 로고    scopus 로고
    • Clustering in massive data sets
    • J. Abello, P.M. Pardalos, and M. G. C. Reisende, Eds., Kluwer, Dordrecht, The Netherlands
    • F. Murtagh, in Handbook of Massive Data Sets, J. Abello, P. M. Pardalos, and M. G. C. Reisende, Eds., Kluwer, Dordrecht, The Netherlands, 2001, pp. 401-545. Clustering in Massive Data Sets.
    • (2001) Handbook of Massive Data Sets , pp. 401-545
    • Murtagh, F.1
  • 42
    • 44649194671 scopus 로고
    • Divisive vs. Agglomerative average linkage hierarchical clustering
    • W. Gaul and M. Schader, Eds., Elsevier Science (North-Holland), Amsterdam
    • D. W. Matula, in Classification as a Tool of Research, W. Gaul and M. Schader, Eds., Elsevier Science (North-Holland), Amsterdam, 1986, pp. 289-301. Divisive vs. Agglomerative Average Linkage Hierarchical Clustering.
    • (1986) Classification as a Tool of Research , pp. 289-301
    • Matula, D.W.1
  • 43
    • 0022439920 scopus 로고
    • Monte Carlo comparison of six hierarchical clustering methods on random data
    • N. C.Jain, A. Indrayan, and L. R. Goel, Pattern Recognition, 19 (I), 95 (1986). Monte Carlo Comparison of Six Hierarchical Clustering Methods on Random Data.
    • (1986) Pattern Recognition, 19 (1) , vol.95
    • Jain, N.C.1    Indrayan, A.2    Goel, L.R.3
  • 44
    • 0024900082 scopus 로고
    • New combinatorial clustering methods
    • J. Podani, Vegetatio, 81, 61 (1989). New Combinatorial Clustering Methods.
    • (1989) Vegetatio , vol.81 , pp. 61
    • Podani, J.1
  • 45
    • 0002561987 scopus 로고
    • Basic procedures in hierarchical cluster analysis
    • J. Devillers and W. Karcher, Eds., Kluwer, Dordrecht, The Netherlands
    • M. Roux, in Applied Multivariate Analysis in SAR and Environmental Studies, J. Devillers and W. Karcher, Eds., Kluwer, Dordrecht, The Netherlands, 1991, pp. 115-135. Basic Procedures in Hierarchical Cluster Analysis.
    • (1991) Applied Multivariate Analysis in SAR and Environmental Studies , pp. 115-135
    • Roux, M.1
  • 46
    • 0024686546 scopus 로고
    • Hierarchic document clustering using ward's method
    • A. El-Hamdouchi and P. Willett, Computer J., 32, 220 (1989). Hierarchic Document Clustering using Ward's Method.
    • (1989) Computer J. , vol.32 , pp. 220
    • El-Hamdouchi, A.1    Willett, P.2
  • 47
    • 0024619684 scopus 로고
    • Efficiency of hierarchical agglomerative clustering using the ICL distributed array processor
    • E. M. Rasmussen and P. Willett, J. Doc, 45 (2), 1 (1989). Efficiency of Hierarchical Agglomerative Clustering Using the ICL Distributed Array Processor.
    • (1989) J. Doc. , vol.45 , Issue.1 , pp. 2
    • Rasmussen, E.M.1    Willett, P.2
  • 48
    • 0028496956 scopus 로고
    • Similarity searching and clustering of chemical-structure databases using molecular property data
    • G. M. Downs, P. Willett, and W. FisanickJ. Chem. Inf. Comput. Sci., 34 (5), 1094 (1994). Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data.
    • (1994) J. Chem. Inf. Comput. Sci. , vol.34 , Issue.5 , pp. 1094
    • Downs, G.M.1    Willett, P.2    Fisanick, W.3
  • 49
    • 33745907609 scopus 로고    scopus 로고
    • A clustering algorithm for categorical attributes
    • Bell Laboratories, Murray Hill, NJ
    • S. Guha, R. Rastogi, and K. Shim, Technical Report, Bell Laboratories, Murray Hill, NJ, 1997. A Clustering Algorithm for Categorical Attributes.
    • (1997) Technical Report
    • Guha, S.1    Rastogi, R.2    Shim, K.3
  • 50
    • 0034228041 scopus 로고    scopus 로고
    • ROCK: A robust clustering algorithm for categorical attributes
    • S. Guha, R. Rastogi, and K. Shim, Inf. Systems, 25 (5), 345 (2000). ROCK: A Robust Clustering Algorithm for Categorical Attributes.
    • (2000) Systems Inf. , vol.25 , Issue.5 , pp. 345
    • Guha, S.1    Rastogi, R.2    Shim, K.3
  • 53
    • 3543113176 scopus 로고    scopus 로고
    • Multilevel refinement for hierarchical clustering
    • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN
    • G. Karypis, E.-H. Han, and V. Kumar, Technical Report No. 99-1020, Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, 1999. Multilevel Refinement for Hierarchical Clustering.
    • (1999) Technical Report No. 99-1020
    • Karypis, G.1    Han, E.-H.2    Kumar, V.3
  • 54
    • 0004140078 scopus 로고    scopus 로고
    • An analysis of recent work on clustering algorithms
    • Department of Computer Science & Engineering, University of Washington, Seattle, WA
    • D. Fasulo, Technical Report No. 01-03-102, Department of Computer Science & Engineering, University of Washington, Seattle, WA, 1999. An Analysis of Recent Work on Clustering Algorithms.
    • (1999) Technical Report No. 01-03-102
    • Fasulo, D.1
  • 57
    • 0030355301 scopus 로고    scopus 로고
    • The weighted sum of split and diameter clustering
    • Y. Wang, H.Yan, and C.SriskandarajahJ. Classification, 13,231 (1996). The Weighted Sum of Split and Diameter Clustering.
    • (1996) J. Classification , vol.13 , pp. 231
    • Wang, Y.1    Yan, H.2    Sriskandarajah, C.3
  • 58
    • 0004164256 scopus 로고    scopus 로고
    • A comparison of document clustering techniques
    • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN
    • M. Steinbach, G. Karypis, and V. Kumar, Technical Report 00-034, Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, 2000. A Comparison of Document Clustering Techniques.
    • (2000) Technical Report 00-034
    • Steinbach, M.1    Karypis, G.2    Kumar, V.3
  • 59
    • 0030769940 scopus 로고    scopus 로고
    • Analysis of a large structure-activity data set using recursive partitioning
    • D. M. Hawkins, S. S. Young, and A. Rusinko, Quant. Struct.-Act. Relat., 16, 396 (1997). Analysis of a Large Structure-Activity Data Set Using Recursive Partitioning.
    • (1997) Quant. Struct.-Act. Relat. , vol.16 , pp. 396
    • Hawkins, D.M.1    Young, S.S.2    Rusinko, A.3
  • 60
    • 0000128084 scopus 로고    scopus 로고
    • Recursive partitioning analysis of a large structure-activity data set using three-dimen-sional descriptors
    • X. Chen, A. Rusinko, and S. S. Young, J. Chem. Inf. Comput. Sci., 38 (6), 1054 (1998). Recursive Partitioning Analysis of a Large Structure-Activity Data Set Using Three-Dimen-sional Descriptors.
    • (1998) J. Chem. Inf. Comput. Sci. , vol.38 , Issue.6 , pp. 1054
    • Chen, X.1    Rusinko, A.2    Young, S.S.3
  • 61
    • 0001376170 scopus 로고    scopus 로고
    • Potential drugs and nondrugs: Prediction and identification of important structural features
    • M. Wagenerand V. J. van GeeresteinJ. Chem. Inf. Compnt. Sci., 40 (2), 280 (2000). Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features.
    • (2000) J. Chem. Inf. Compnt. Sci. , vol.40 , Issue.2 , pp. 280
    • Wagenerand, M.1    Van Geerestein, V.J.2
  • 62
    • 0000177416 scopus 로고    scopus 로고
    • Results of a new classification algorithm combining k nearest neighbors and recursive partitioning
    • D. W. Miller. Chem. Inf. Compnt. Sci., 41 (1), 168 (2001). Results of a New Classification Algorithm Combining K Nearest Neighbors and Recursive Partitioning.
    • (2001) J. Chem. Inf. Compnt. Sci. , vol.41 , Issue.1 , pp. 168
    • Miller, D.W.1
  • 63
    • 0030157145 scopus 로고    scopus 로고
    • BIRCH: An efficient data clustering method for very large databases
    • T. Zhang, R. Ramakrishnan, and M. Livny, ACM SIGMOD Record, 25 (2), 103 (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases.
    • (1996) ACM SIGMOD Record , vol.25 , Issue.2 , pp. 103
    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3
  • 65
    • 0000635548 scopus 로고    scopus 로고
    • Rational screening set design and compound selection: Cascaded clustering
    • P. R. Menard, R. A. Lewis, and J. S. Mason J. Chem. Inf. Compnt. Sci., 38 (3), 379 (1998). Rational Screening Set Design and Compound Selection: Cascaded Clustering.
    • (1998) J. Chem. Inf. Compnt. Sci. , vol.38 , Issue.3 , pp. 379
    • Menard, P.R.1    Lewis, R.A.2    Mason, J.S.3
  • 67
    • 0034265015 scopus 로고    scopus 로고
    • Identification of groupings of graph theoretical descriptors using a hybrid cluster analysis approach
    • S. L. Taraviras, O. Ivanciuc, and D. Cabrol-Bass, J. Chem. Inf. Compnt. Sci., 40 (5), 1128 (2000). Identification of Groupings of Graph Theoretical Descriptors Using a Hybrid Cluster Analysis Approach.
    • (2000) J. Chem. Inf. Compnt. Sci. , vol.40 , Issue.5 , pp. 1128
    • Taraviras, S.L.1    Ivanciuc, O.2    Cabrol-Bass, D.3
  • 68
    • 0018183618 scopus 로고
    • Agglomerative clustering using the concept of mutual nearest neighborhood
    • K. C. Gowda and G. Krishna, Pattern Recognition, 10 (2), 105 (1978). Agglomerative Clustering Using the Concept of Mutual Nearest Neighborhood.
    • (1978) Pattern Recognition , vol.10 , Issue.2 , pp. 105
    • Gowda, K.C.1    Krishna, G.2
  • 70
    • 0000014486 scopus 로고
    • Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications
    • E. Forgy, Biometrics, 21, 768 (1965). Cluster Analysis of Multivariate Data: Efficiency vs. Interpretability of Classifications.
    • (1965) Biometrics , vol.21 , pp. 768
    • Forgy, E.1
  • 71
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • University of California Press, Berkeley, CA
    • J. MacQueen, in Proceedings of the Sth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, CA, 1967, Vol.1, pp. 281-297. Some Methods for Classification and Analysis of Multivariate Observations.
    • (1967) Proceedings of the Sth Berkeley Symposium on Mathematical Statistics and Probability , vol.1 , pp. 281-297
    • MacQueen, J.1
  • 72
    • 0001138328 scopus 로고
    • A k-means clustering algorithm
    • J. A. Hartigan and M. A. Wong, Appl. Stat., 28,100 (1979). A K-Means Clustering Algorithm.
    • (1979) Appl. Stat. , vol.28 , pp. 100
    • Hartigan, J.A.1    Wong, M.A.2
  • 73
    • 85007280785 scopus 로고
    • Computational experiences with the exchange method
    • H. Spaeth, Eur.]. Operat. Res., 1,23 (1977). Computational Experiences with the Exchange Method.
    • (1977) Eur. J. Operat. Res. , vol.1 , pp. 23
    • Spaeth, H.1
  • 75
    • 0024475950 scopus 로고
    • Multidimensional data clustering utilizing hybrid search strategies
    • M. A. Ismail and M. S. Kamel, Pattern Recognition, 22, 75 (1989). Multidimensional Data Clustering Utilizing Hybrid Search Strategies.
    • (1989) Pattern Recognition , vol.22 , pp. 75
    • Ismail, M.A.1    Kamel, M.S.2
  • 76
    • 0025757548 scopus 로고
    • A clustering algorithm for datasets with a large number of classes
    • Q. Zhang, Q. R. Wang, and R. D. Boyle, Pattern Recognition, 24 (4), 331 (1991). A Clustering Algorithm for Datasets with a Large Number of Classes.
    • (1991) Pattern Recognition , vol.24 , Issue.4 , pp. 331
    • Zhang, Q.1    Wang, Q.R.2    Boyle, R.D.3
  • 77
    • 27144536001 scopus 로고    scopus 로고
    • Extensions to the k-means algorithm for clustering large data sets with categorical values
    • Z. Huang, Int. J. Data Mining Knotuledge Disc, 2 (3), 283 (1998). Extensions to the K-Means Algorithm for Clustering Large Data Sets with Categorical Values.
    • (1998) Int. J. Data Mining Knowledge Disc. , vol.2 , Issue.3 , pp. 283
    • Huang, Z.1
  • 78
    • 0026304372 scopus 로고
    • A new clustering algorithm with multiple runs of iterative procedures
    • Q. Zhang and R. D. Boyle, Pattern Recognition, 24 (9), 835 (1991). A New Clustering Algorithm with Multiple Runs of Iterative Procedures.
    • (1991) Pattern Recognition , vol.24 , Issue.9 , pp. 835
    • Zhang, Q.1    Boyle, R.D.2
  • 79
    • 77949933537 scopus 로고    scopus 로고
    • A fast and robust general purpose clustering algorithm
    • Department of Computer Science & Software Engineering, University of Newcastle, Callaghan, NSW 2308, Australia
    • V. Estivell-Castro and J. Yang, Technical Report No. 99-103, Department of Computer Science & Software Engineering, University of Newcastle, Callaghan, NSW 2308, Australia. A Fast and Robust General Purpose Clustering Algorithm.
    • Technical Report No. 99-103
    • Estivell-Castro, V.1    Yang, J.2
  • 81
    • 0031232030 scopus 로고    scopus 로고
    • Experimental designs for selecting molecules from large chemical databases
    • R. E. Higgs, K. G. Bemis, I. A. Watson, and J. H. Wikel, ;. Chem. Inf. Compnt. Sci., 37 (5), 861 (1997). Experimental Designs for Selecting Molecules from Large Chemical Databases.
    • (1997) J. Chem. Inf. Comput. Sci. , vol.37 , Issue.5 , pp. 861
    • Higgs, R.E.1    Bemis, K.G.2    Watson, I.A.3    Wikel, J.H.4
  • 84
    • 33847457966 scopus 로고
    • An examination of the effect of six types of error perturbation on fifteen clustering algorithms
    • G. W. Milligan, Psychometrika, 45 (3), 325 (1980). An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms.
    • (1980) Psychometrika , vol.45 , Issue.3 , pp. 325
    • Milligan, G.W.1
  • 85
    • 0029678997 scopus 로고    scopus 로고
    • Iterative optimization and simplification of hierarchical clusterings
    • D. Fisher, J. Artif. Intel], Res., 4, 147 (1996). Iterative Optimization and Simplification of Hierarchical Clusterings.
    • (1996) J. Artif. Intell. Res. , vol.4 , pp. 147
    • Fisher, D.1
  • 87
    • 0027453616 scopus 로고
    • Model-based gaussian and non-gaussian clustering
    • J. D. Banfield and A. E. Raftery, Biometrics, 49, 803 (1993). Model-Based Gaussian and Non-Gaussian Clustering.
    • (1993) Biometrics , vol.49 , pp. 803
    • Banfield, J.D.1    Raftery, A.E.2
  • 88
    • 0032269108 scopus 로고
    • How many clusters? which clustering method? answers via model-based cluster analysis
    • C. Fraley and A. E. Raftery, Computer J., 41 (S), 578 (1988). How Many Clusters? Which Clustering Method? Answers via Model-Based Cluster Analysis.
    • (1988) Computer J. , vol.41 , Issue.8 , pp. 578
    • Fraley, C.1    Raftery, A.E.2
  • 92
    • 84956852642 scopus 로고    scopus 로고
    • OPTICS-OF: Identifying local outliers
    • Springer
    • In Lecture Notes in Computer Science, Springer, 1704, 262-270 (1999). OPTICS-OF: Identifying Local Outliers.
    • (1999) Lecture Notes in Computer Science , vol.1704 , Issue.262-270 , pp. 1999
  • 97
    • 0001919798 scopus 로고
    • Graph theoretic techniques for cluster analysis algorithms
    • J. van Ryzin, Ed., Academic Press
    • D. W. Matula, in Classification and Clustering, J. van Ryzin, Ed., Academic Press, 1977, pp. 95-129. Graph Theoretic Techniques for Cluster Analysis Algorithms.
    • (1977) Classification and Clustering , pp. 95-129
    • Matula, D.W.1
  • 98
    • 0032728081 scopus 로고    scopus 로고
    • Clustering gene expression patterns
    • A. Ben-Dor, R.Shamir, and Z. Yakhini, J. Comput. Biol., 6 (3/4), 281 (1999). Clustering Gene Expression Patterns.
    • (1999) J. Comput. Biol. , vol.6 , Issue.3-4 , pp. 281
    • Ben-Dor, A.1    Shamir, R.2    Yakhini, Z.3
  • 101
    • 32844455920 scopus 로고    scopus 로고
    • Graph-based hierarchical conceptual clustering
    • I. Jonyer, L. B. Holder, and D. J. Cook, in Proceedings of the 13th Annual Florida AI Research Symposium, pp. 91-95, 2000 (http://www-cse.uta.edu/ ~cook/pubs). Graph-Based Hierarchical Conceptual Clustering.
    • (2000) th Annual Florida AI Research Symposium , pp. 91-95
    • Jonyer, I.1    Holder, L.B.2    Cook, D.J.3
  • 102
    • 0020848951 scopus 로고
    • A survey of recent advances in hierarchical clustering algorithms
    • F. Murtagh, Computer J., 26 (4), 354 (1983). A Survey of Recent Advances in Hierarchical Clustering Algorithms.
    • (1983) Computer J. , vol.26 , Issue.4 , pp. 354
    • Murtagh, F.1
  • 103
    • 84988112537 scopus 로고
    • Automatic classification of chemical structure databases using a highly parallel array processor
    • E. M. Rasmussen, G. M. Downs, and P. WillettJ. Comput. Chem., 9 (4), 378 (1988). Automatic Classification of Chemical Structure Databases Using a Highly Parallel Array Processor.
    • (1988) J. Comput. Chem. , vol.9 , Issue.4 , pp. 378
    • Rasmussen, E.M.1    Downs, G.M.2    Willett, P.3
  • 104
    • 0024718536 scopus 로고
    • Parallel clustering algorithms
    • X. Li and Z. Fang, Parallel Computing, 11, 275 (1989). Parallel Clustering Algorithms.
    • (1989) Parallel Computing , vol.11 , pp. 275
    • Li, X.1    Fang, Z.2
  • 105
    • 0025521828 scopus 로고
    • Parallel algorithms for hierarchical clustering and cluster validity
    • X. Li, IEEE Trans. Pattern Anal. Machine Intelligence, 12 (11), 1088 (1990). Parallel Algorithms for Hierarchical Clustering and Cluster Validity.
    • (1990) IEEE Trans. Pattern Anal. Machine Intelligence , vol.12 , Issue.11 , pp. 1088
    • Li, X.1
  • 106
    • 0026938525 scopus 로고
    • Parallel algorithms for hierarchical clustering and cluster validity
    • F. Murtagh, IEEE Trans. Pattern Anal. Machine Intelligence, 14 (10), 1056 (1992). Comments on "Parallel Algorithms for Hierarchical Clustering and Cluster Validity".
    • (1992) IEEE Trans. Pattern Anal. Machine Intelligence , vol.14 , Issue.10 , pp. 1056
    • Murtagh, F.1
  • 107
    • 0005389587 scopus 로고
    • Parallel algorithms for hierarchical clustering
    • University of California, Berkeley, CA
    • C. F. Olson, Technical Report CSD-94-786, University of California, Berkeley, CA, 1994. Parallel Algorithms for Hierarchical Clustering.
    • (1994) Technical Report CSD-94-786
    • Olson, C.F.1
  • 108
    • 0026163518 scopus 로고
    • Clustering a large number of compounds. 2. Using a connection machine
    • R. Whaley and L. Hodes, J. Chem. Inf. Comput. Sci., 31 (2), 345 (1991). Clustering a Large Number of Compounds. 2. Using a Connection Machine.
    • (1991) J. Chem. Inf. Comput. Sci. , vol.31 , Issue.2 , pp. 345
    • Whaley, R.1    Hodes, L.2
  • 109
    • 0034592784 scopus 로고    scopus 로고
    • Efficient clustering of high dimensional data sets with application to reference matching
    • MIT Press, Cambridge, MA
    • A. McCallum, K. Nigam, and L. H. Ungar, in Advances in Knotvledge Discovery and Data Mining, MIT Press, Cambridge, MA, 2000. Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching.
    • (2000) Advances in Knotvledge Discovery and Data Mining
    • McCallum, A.1    Nigam, K.2    Ungar, L.H.3
  • 111
    • 24444450990 scopus 로고
    • A comparison of some hierarchical agglomerative clustering algorithms for structure-property correlation
    • P. Willett, Anal. Chim. Acta, 136, 29 (1982). A Comparison of Some Hierarchical Agglomerative Clustering Algorithms for Structure-Property Correlation.
    • (1982) Anal. Chim. Acta , vol.136 , pp. 29
    • Willett, P.1
  • 112
    • 19644375766 scopus 로고
    • A comparison of some hierarchical monothetic divisive clustering algorithms for structure-property correlation
    • V. Rubin and P. Willett, Anal. Chim. Acta, 151, 161 (1983). A Comparison of Some Hierarchical Monothetic Divisive Clustering Algorithms for Structure-Property Correlation.
    • (1983) Anal. Chim. Acta , vol.151 , pp. 161
    • Rubin, V.1    Willett, P.2
  • 113
    • 0021373652 scopus 로고
    • Evaluation of relocation clustering algorithms for the automatic classification of chemical structures
    • P. Willett, J. Chem. Inf. Comput. Sci., 24 (1), 29 (1984). Evaluation of Relocation Clustering Algorithms for the Automatic Classification of Chemical Structures.
    • (1984) J. Chem. Inf. Comput. Sci. , vol.24 , Issue.1 , pp. 29
    • Willett, P.1
  • 114
    • 0002989195 scopus 로고
    • New index for clustering tendency and its application to chemical problems
    • R. G. Lawson and P. C. Jurs, J. Chem. Inf. Comput. Sci., 30 (1), 36 (1990). New Index for Clustering Tendency and Its Application to Chemical Problems.
    • (1990) J. Chem. Inf. Comput. Sci. , vol.30 , Issue.1 , pp. 36
    • Lawson, R.G.1    Jurs, P.C.2
  • 115
    • 0022539078 scopus 로고
    • On the significance of clusters in graphical display of structure-activity data
    • J. W. McFarland and D.J. Gans,J. Med. Chem., 29,505-514 (1986). On the Significance of Clusters in Graphical Display of Structure-Activity Data.
    • (1986) J. Med. Chem. , vol.29 , pp. 505-514
    • McFarland, J.W.1    Gans, D.J.2
  • 116
    • 3242783231 scopus 로고    scopus 로고
    • Clusterability detection and initial seed selection in large data sets
    • Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY
    • S. Epter, M. Krishnamoorthy, and M. Zaki, Technical Report No. 99-106, Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY, 1999. Clusterability Detection and Initial Seed Selection in Large Data Sets.
    • (1999) Technical Report No. 99-106
    • Epter, S.1    Krishnamoorthy, M.2    Zaki, M.3
  • 119
    • 34250115918 scopus 로고
    • An examination of procedures for determining the number of clusters in a data set
    • G. W. Milligan and M. C. Cooper, Psychometrika, 50 (2), 159 (1985). An Examination of Procedures for Determining the Number of Clusters in a Data Set.
    • (1985) Psychometrika , vol.50 , Issue.2 , pp. 159
    • Milligan, G.W.1    Cooper, M.C.2
  • 120
    • 0000462999 scopus 로고    scopus 로고
    • Comparison of 2D fingerprint types and hierarchy level selection methods for structural grouping using ward's clustering
    • D. J. Wild and C. J. Blankley,J. Chem. Inf. Comput. Sci., 40 (1), 155 (2000). Comparison of 2D Fingerprint Types and Hierarchy Level Selection Methods for Structural Grouping Using Ward's Clustering.
    • (2000) J. Chem. Inf. Comput. Sci. , vol.40 , Issue.1 , pp. 155
    • Wild, D.J.1    Blankley, C.J.2
  • 121
    • 0029831680 scopus 로고    scopus 로고
    • An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally-related subfamilies
    • L. A. Kelley, S. P. Gardner, and M.J. Sutcliffe, Protein Eng., 9, 1063 (1996). An Automated Approach for Clustering an Ensemble of NMR-Derived Protein Structures into Conformationally-Related Subfamilies.
    • (1996) Protein Eng. , vol.9 , pp. 1063
    • Kelley, L.A.1    Gardner, S.P.2    Sutcliffe, M.J.3
  • 122
    • 0000228352 scopus 로고
    • A Monte Carlo study of thirty internal criterion measures for cluster analysis
    • G. W. Milligan, Psychometrika, 46 (2), 187 (1981). A Monte Carlo Study of Thirty Internal Criterion Measures for Cluster Analysis.
    • (1981) Psychometrika , vol.46 , Issue.2 , pp. 187
    • Milligan, G.W.1
  • 123
    • 84972893020 scopus 로고
    • A dendrite method for cluster analysis
    • T. Calinski and J. Harabasz, Commun. Stat., 3 (1), 1 (1974). A Dendrite Method for Cluster Analysis.
    • (1974) Commun. Stat. , vol.3 , Issue.1 , pp. 1
    • Calinski, T.1    Harabasz, J.2
  • 125
    • 3743106309 scopus 로고    scopus 로고
    • Exploiting molecular diversity: Pharmacophore searching and compound clustering
    • (Proceedings 9th European QSAR Meeting, Lausanne, Switzerland, 1996), H. van de Waterbeemd, B. Testa, and G. Folkers, Eds., Wiley-VCH, Basel, Switzerland
    • V. J. van Geerestein, H. Hamersma, and S. P. van Helden, in Computer-Assisted Lead Finding and Optimization (Proceedings 9th European QSAR Meeting, Lausanne, Switzerland, 1996), H. van de Waterbeemd, B. Testa, and G. Folkers, Eds., Wiley-VCH, Basel, Switzerland, 1997, pp. 159-178. Exploiting Molecular Diversity: Pharmacophore Searching and Compound Clustering.
    • (1997) Computer-Assisted Lead Finding and Optimization , pp. 159-178
    • Van Geerestein, V.J.1    Hamersma, H.2    Van Helden, S.P.3
  • 126
    • 0033106944 scopus 로고    scopus 로고
    • VisualiSAR: A web-based application for clustering, structure browsing, and structure-activity relationship study
    • D.J. Wild and C.J. Blankley,J. Mol. Graphics Modell., 17(2), 85 (1999). VisualiSAR: A Web-Based Application for Clustering, Structure Browsing, and Structure-Activity Relationship Study.
    • (1999) J. Mol. Graphics Modell. , vol.17 , Issue.2 , pp. 85
    • Wild, D.J.1    Blankley, C.J.2
  • 129
    • 0032619115 scopus 로고    scopus 로고
    • Application of nearest-neighbor and cluster analyses in pharmaceutical lead discovery
    • D. T. Stanton, T. W. Morris, S. Roychoudhury, and C. N. Parker, J. Chem. Inf. Comput. Sci., 39 (1), 21 (1999). Application of Nearest-Neighbor and Cluster Analyses in Pharmaceutical Lead Discovery.
    • (1999) J. Chem. Inf. Comput. Sci. , vol.39 , Issue.1 , pp. 21
    • Stanton, D.T.1    Morris, T.W.2    Roychoudhury, S.3    Parker, C.N.4
  • 130
    • 0000610487 scopus 로고    scopus 로고
    • Evaluation and use of BCUT descriptors in QSAR and QSPR studies
    • D. T. Stanton, J. Chem. Inf. Comptit. Sci., 39 (1), 11 (1999). Evaluation and Use of BCUT Descriptors in QSAR and QSPR Studies.
    • (1999) J. Chem. Inf. Comptut. Sci. , vol.39 , Issue.1 , pp. 11
    • Stanton, D.T.1
  • 131
    • 15744363581 scopus 로고    scopus 로고
    • Metric validation and the receptor-relevant subspace concept
    • R. S. Pearlman and K. M. Smith,;. Chem. Inf. Comput. Sci., 39 (1) 28-35 (1999). Metric Validation and the Receptor-Relevant Subspace Concept.
    • (1999) J. Chem. Inf. Comput. Sci. , vol.39 , Issue.1 , pp. 28-35
    • Pearlman, R.S.1    Smith, K.M.2
  • 132
    • 0024662190 scopus 로고
    • Clustering a large number of compounds. 1. Establishing the method on an initial sample
    • L. Hodes, ;. Chem. Inf. Comput. Sci., 29 (2), 66 (1989). Clustering a Large Number of Compounds. 1. Establishing the Method on an Initial Sample.
    • (1989) J. Chem. Inf. Comput. Sci. , vol.29 , Issue.2 , pp. 66
    • Hodes, L.1
  • 133
    • 0026164062 scopus 로고
    • Clustering a large number of compounds. 3. The limits of classification
    • R. Whaley and L. Hodes, J. Chem. Inf. Comput. Sci., 31 (2), 347 (1991). Clustering a Large Number of Compounds. 3. The Limits of Classification.
    • (1991) J. Chem. Inf. Comput. Sci. , vol.31 , Issue.2 , pp. 347
    • Whaley, R.1    Hodes, L.2
  • 134
    • 0032671931 scopus 로고    scopus 로고
    • Unsupervised data base clustering based on daylight's fingerprint and tanimoto similarity a fast and automated way to cluster small and large data sets
    • D. ButinaJ. Chem. Inf. Comput. Sci., 39 (4), 747 (1999). Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity a Fast and Automated Way to Cluster Small and Large Data Sets.
    • (1999) J. Chem. Inf. Comput. Sci. , vol.39 , Issue.4 , pp. 747
    • Butina, D.1
  • 135
    • 0006128054 scopus 로고    scopus 로고
    • Lead discovery using stochastic cluster analysis (SCA): A new method for clustering structurally similar compounds
    • C. H. Reynolds, R. Druker, and L. B. PfahlerJ. Chem. Inf. Comput. Sci., 38 (2), 305 (1998). Lead Discovery Using Stochastic Cluster Analysis (SCA): A New Method for Clustering Structurally Similar Compounds.
    • (1998) J. Chem. Inf. Comput. Sci. , vol.38 , Issue.2 , pp. 305
    • Reynolds, C.H.1    Druker, R.2    Pfahler, L.B.3
  • 136
    • 0000493839 scopus 로고    scopus 로고
    • Balancing representativeness against diversity using optimizable k-dissimilarity and hierarchical clustering
    • R. D. Clark and W. J. Langton, J. Chem. Inf. Comput. Sci., 38 (6), 1079 (1998). Balancing Representativeness Against Diversity Using Optimizable K-dissimilarity and Hierarchical Clustering.
    • (1998) J. Chem. Inf. Comput. Sci. , vol.38 , Issue.6 , pp. 1079
    • Clark, R.D.1    Langton, W.J.2
  • 137
    • 33746132663 scopus 로고
    • Implementation of nonhierarchic cluster-analysis methods in chemical information systems; selection of compounds for biological testing and clustering of substructure search output
    • P. Willett, V. Winterman, and D. BawdenJ. Chem. Inf. Comput. Sci., 26 (3), 109 (1986). Implementation of Nonhierarchic Cluster-Analysis Methods in Chemical Information Systems; Selection of Compounds for Biological Testing and Clustering of Substructure Search Output.
    • (1986) J. Chem. Inf. Comput. Sci. , vol.26 , Issue.3 , pp. 109
    • Willett, P.1    Winterman, V.2    Bawden, D.3
  • 138
    • 0000892020 scopus 로고    scopus 로고
    • Clustering large databases of compounds: Using the MDL "keys" as structural descriptors
    • M. J. McGregor and P. V. PallaiJ. Chem. Inf. Comput. Sci., 37 (3), 443 (1997). Clustering Large Databases of Compounds: Using the MDL "Keys" as Structural Descriptors.
    • (1997) J. Chem. Inf. Comput. Sci. , vol.37 , Issue.3 , pp. 443
    • McGregor, M.J.1    Pallai, P.V.2
  • 140
    • 0642270782 scopus 로고    scopus 로고
    • Cluster-based selection
    • P. Willett, Ed., Perspectives in Drug Discovery and Design, Kluwer/ESCOM, Dordrecht, The Netherlands
    • J. B. Dunbar, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol.7/8, Kluwer/ESCOM, Dordrecht, The Netherlands, 1997, pp. 51-63. Cluster-Based Selection.
    • (1997) Computational Methods for the Analysis of Molecular Diversity , vol.7-8 , pp. 51-63
    • Dunbar, J.B.1
  • 141
    • 0041640133 scopus 로고
    • The use of similarity and clustering techniques for the prediction of molecular properties
    • J. Devillers and W. Karcher, Eds., Kluwer, Dordrecht, The Netherlands
    • G. M. Downs and P. Willett, in Applied Multivariate Analysis in SAR and Environmental Studies, J. Devillers and W. Karcher, Eds., Kluwer, Dordrecht, The Netherlands, 1991, pp. 247-279. The Use of Similarity and Clustering Techniques for the Prediction of Molecular Properties.
    • (1991) Applied Multivariate Analysis in SAR and Environmental Studies , pp. 247-279
    • Downs, G.M.1    Willett, P.2
  • 142
    • 0343887344 scopus 로고
    • An investigation of clustering as a tool in quantitative structure-activity relationships (QSARs)
    • J. Nouwen and B. Hansen, SAR and QSAR in Environmental Research, 4, 1 (1995). An Investigation of Clustering as a Tool in Quantitative Structure-Activity Relationships (QSARs).
    • (1995) SAR and QSAR in Environmental Research , vol.4 , pp. 1
    • Nouwen, J.1    Hansen, B.2
  • 144
    • 0025425484 scopus 로고
    • Cluster analysis of acrylates to guide sampling for toxicity testing
    • R. G. Lawson and P. C. Jurs, J. Chem. Inf. Comput. Sci., 30 (2), 137 (1990). Cluster Analysis of Acrylates to Guide Sampling for Toxicity Testing.
    • (1990) J. Chem. Inf. Comput. Sci. , vol.30 , Issue.2 , pp. 137
    • Lawson, R.G.1    Jurs, P.C.2
  • 145
    • 33751136693 scopus 로고
    • Assessing similarity and diversity of combinatorial libraries by spatial autocorrelation functions and neural networks
    • J. Sadowski, M. Wagener, and J. Gasteiger, Angew. Client., Int. Ed. Engl., 34 (23/24), 2674 (1995/1996). Assessing Similarity and Diversity of Combinatorial Libraries by Spatial Autocorrelation Functions and Neural Networks.
    • (1995) Angew. Client. Int. Ed. Engl. , vol.34 , Issue.23-24 , pp. 2674
    • Sadowski, J.1    Wagener, M.2    Gasteiger, J.3


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