Reviews in Computational Chemistry Volume 18

More documents

Recommendations

Info

References 37 59. M. Wagener and V. J. van Geerestein, J. Chem. Inf. Comput. Sci., 40 (2), 280 (2000). Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features. 60. D. W. Miller, J. Chem. Inf. Comput. Sci., 41 (1), 168 (2001). Results of a New Classification Algorithm Combining K Nearest Neighbors and Recursive Partitioning. 61. T. Zhang, R. Ramakrishnan, and M. Livny, ACM SIGMOD Record, 25 (2), 103 (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. 62. V. Ganti, R. Ramakrishnan, J. Gehrke, A. Powell, and J. French, Proceedings of the 15th International Conference on Data Engineering, Sydney, Australia, 1999, pp. 502–511. Clustering Large Datasets in Arbitrary Metric Spaces. 63. P. R. Menard, R. A. Lewis, and J. S. Mason, J. Chem. Inf. Comput. Sci., 38 (3), 379 (1998). Rational Screening Set Design and Compound Selection: Cascaded Clustering. 64. T. N. Doman, J. M. Cibulskis, M. J. Cibulskis, P. D. McCray, and D. P. Spangler, J. Chem. Inf. Comput. Sci., 36 (6), 1195 (1996). Algorithm5: A Technique for Fuzzy Clustering of Chemical Inventories. 65. S. L. Taraviras, O. Ivanciuc, and D. Cabrol-Bass, J. Chem. Inf. Comput. Sci., 40 (5), 1128 (2000). Identification of Groupings of Graph Theoretical Descriptors Using a Hybrid Cluster Analysis Approach. 66. K. C. Gowda and G. Krishna, Pattern Recognition, 10 (2), 105 (1978). Agglomerative Clustering Using the Concept of Mutual Nearest Neighborhood. 67. R. Dugad and N. Ahuja, IEEE International Conference on Acoustics Speech and Signal Processing, 5, 2761 (1998). Unsupervised Multidimensional Hierarchical Clustering. 68. E. Forgy, Biometrics, 21, 768 (1965). Cluster Analysis of Multivariate Data: Efficiency vs. Interpretability of Classifications. 69. J. MacQueen, in Proceedings of the 5th 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. 70. J. A. Hartigan and M. A. Wong, Appl. Stat., 28, 100 (1979). A K-Means Clustering Algorithm. 71. H. Spaeth, Eur. J. Operat. Res., 1, 23 (1977). Computational Experiences with the Exchange Method. 72. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973. 73. M. A. Ismail and M. S. Kamel, Pattern Recognition, 22, 75 (1989). Multidimensional Data Clustering Utilizing Hybrid Search Strategies. 74. 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. 75. Z. Huang, Int. J. Data Mining Knowledge Disc., 2 (3), 283 (1998). Extensions to the K-Means Algorithm for Clustering Large Data Sets with Categorical Values. 76. Q. Zhang and R. D. Boyle, Pattern Recognition, 24 (9), 835 (1991). A New Clustering Algorithm with Multiple Runs of Iterative Procedures. 77. V. Estivell-Castro and J. Yang, Technical Report No. 99-03, Department of Computer Science & Software Engineering, University of Newcastle, Callaghan, NSW 2308, Australia. A Fast and Robust General Purpose Clustering Algorithm. 78. R. T. Ng and J. Han, in Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, 1994, pp. 144–155. Efficient and Effective Clustering Methods for Spatial Data Mining. 79. R. E. Higgs, K. G. Bemis, I. A. Watson, and J. H. Wikel, J. Chem. Inf. Comput. Sci., 37 (5), 861 (1997). Experimental Designs for Selecting Molecules from Large Chemical Databases. 80. M. Snarey, N. K. Terrett, P. Willett, and D. J. Wilton, J. Mol. Graphics Modell., 15, 372 (1997). Comparison of Algorithms for Dissimilarity-Based Compound Selection. 81. D. Fisher, L. Xu, and N. Zard, in Proceedings of the 6th International Workshop on Machine Learning, Morgan Kaufmann, Aberdeen, UK, 1992, pp. 163–168. Ordering Effects in Clustering.
38 Clustering Methods and Their Uses in Computational Chemistry 82. G. W. Milligan, Psychometrika, 45 (3), 325 (1980). An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms. 83. D. Fisher, J. Artif. Intell. Res., 4, 147 (1996). Iterative Optimization and Simplification of Hierarchical Clusterings. 84. G. J. McLachlan and T. Krishnan, The EM Algorithm and Extensions, Wiley, New York, 1997. 85. J. D. Banfield and A. E. Raftery, Biometrics, 49, 803 (1993). Model-Based Gaussian and Non-Gaussian Clustering. 86. C. Fraley and A. E. Raftery, Computer J., 41 (8), 578 (1988). How Many Clusters? Which Clustering Method? Answers via Model-Based Cluster Analysis. 87. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226–231. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. 88. M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Philadelphia, PA, 1999, pp. 49–60. OPTICS: Ordering Points to Identify Clustering Structure. 89. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, Proceedings of the Conference on Data Mining and Knowledge Discovery, Prague, Czech Repub., 1999, in Lecture Notes in Computer Science, Springer, 1704, 262–270 (1999). OPTICS-OF: Identifying Local Outliers. 90. M. M. Breunig, H.-P. Kriegel, and J. Sander, in Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases, Lyon, France, 2000. Fast Hierarchical Clustering Based on Compressed Data and OPTICS. 91. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, WA, 1998, pp. 94–105. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. 92. C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park, in Proceedings ACM SIGMOD International Conference on Management of Data, Philadelphia, PA, 1999, pp. 61–72. Fast Algorithms for Projected Clustering. 93. H. S. Nagesh, M.Sc. Thesis, Northwestern University of Illinois, Evanston, IL, 1999. High Performance Subspace Clustering for Massive Data Sets. 94. D. W. Matula, in Classification and Clustering, J. van Ryzin, Ed., Academic Press, 1977, pp. 95–129. Graph Theoretic Techniques for Cluster Analysis Algorithms. 95. A. Ben-Dor, R. Shamir, and Z. Yakhini, J. Comput. Biol., 6 (3/4), 281 (1999). Clustering Gene Expression Patterns. 96. E. Hartuv, A. Schmitt, J. Lange, S. Meier-Ewart, H. Lehrach, and R. Shamir, in Proceedings 3rd International Conference on Computational Molecular Biology (RECOMB 99), Lyon, France, 1999. An Algorithm for Clustering cDNAs for Gene Expression Analysis. 97. R. Sharan and R. Shamir, in Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, AAAI Press, Menlo Park, CA, 2000, pp. 307–316. CLICK: A Clustering Algorithm with Application to Gene Expression Analysis. 98. 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. 99. F. Murtagh, Computer J., 26 (4), 354 (1983). A Survey of Recent Advances in Hierarchical Clustering Algorithms. 100. E. M. Rasmussen, G. M. Downs, and P. Willett, J. Comput. Chem., 9 (4), 378 (1988). Automatic Classification of Chemical Structure Databases Using a Highly Parallel Array Processor. 101. X. Li and Z. Fang, Parallel Computing, 11, 275 (1989). Parallel Clustering Algorithms. 102. X. Li, IEEE Trans. Pattern Anal. Machine Intelligence, 12 (11), 1088 (1990). Parallel Algorithms for Hierarchical Clustering and Cluster Validity. 103. F. Murtagh, IEEE Trans. Pattern Anal. Machine Intelligence, 14 (10), 1056 (1992). Comments on ‘‘Parallel Algorithms for Hierarchical Clustering and Cluster Validity’’.
Page 1 and 2:
Reviews in Computational Chemistry
Page 3 and 4:
Kenny B. Lipkowitz Department of Ch
Page 5 and 6:
vi Preface three-dimensional struct
Page 7 and 8:
viii Preface some descriptors and i
Page 9 and 10:
Epilogue and Dedication My associat
Page 11 and 12:
Contents 1. Clustering Methods and
Page 13 and 14:
Contents xv Electron Transfer in Po
Page 15 and 16: Contributors John M. Barnard, Barna
Page 17 and 18: Contributors to Previous Volumes *
Page 19 and 20: Volume 3 (1992) Tamar Schlick, Opti
Page 21 and 22: Volume 7 (1996) Geoffrey M. Downs a
Page 23 and 24: Volume 11 (1997) Mark A. Murcko, Re
Page 25 and 26: T. Daniel Crawford* and Henry F. Sc
Page 27 and 28: Topics Covered in Volumes 1-18 * Ab
Page 29 and 30: Reviews in Computational Chemistry
Page 31 and 32: 2 Clustering Methods and Their Uses
Page 39 and 40: 10 Clustering Methods and Their Use
Page 65: 36 Clustering Methods and Their Use
Page 71 and 72: 42 The Use of Scoring Functions in
Page 79 and 80: Table 1 Reference List for the Most
Page 117 and 118:
CHAPTER 3 Potentials and Algorithms
Page 119 and 120:
Vn (1 + cos(nω + γ)) 2 K θ (θ
Page 121 and 122:
are modified by their environment w
Page 123 and 124:
Table 1 Polarizability Parameters f
Page 125 and 126:
The polarizable point dipole models
Page 127 and 128:
two to three orders of magnitude sl
Page 129 and 130:
M i z i + q i d i k i and shell cha
Page 131 and 132:
Shell Models 103 on estimates of sh
Page 133 and 134:
minimization can be replaced by mor
Page 135 and 136:
The energy required to create a cha
Page 137 and 138:
for all i ði:e:; 8 iÞ: @U @qi l
Page 139 and 140:
where we have used q Cl ¼ qNa. The
Page 141 and 142:
Electronegativity Equalization Mode
Page 143 and 144:
Electronegativity Equalization Mode
Page 145 and 146:
of N molecules is taken as a Hartre
Page 147 and 148:
is treated using variable charges.
Page 149 and 150:
water have been developed, includin
Page 151 and 152:
Applications 123 classical and rigi
Page 153 and 154:
developing polarizable models. A va
Page 155 and 156:
Comparison of the Polarization Mode
Page 157 and 158:
negligible errors in such propertie
Page 159 and 160:
ecome significant at field strength
Page 161 and 162:
noteworthy in this regard because t
Page 163 and 164:
References 135 9. P. Cieplak and P.
Page 165 and 166:
References 137 48. E. L. Pollock an
Page 167 and 168:
References 139 Computational Chemis
Page 169 and 170:
References 141 139. J. Hinze and H.
Page 171 and 172:
References 143 178. J. J. P. Stewar
Page 173 and 174:
References 145 216. M. W. Mahoney a
Page 175 and 176:
CHAPTER 4 New Developments in the T
Page 177 and 178:
Introduction 149 applications). For
Page 179 and 180:
FCWD(∆E ) FCWD(0) ∆E = 0 ∆E
Page 181 and 182:
Introduction 153 Equations [6]-[12]
Page 183 and 184:
Paradigm of Free Energy Surfaces 15
Page 185 and 186:
Paradigm of Free Energy Surfaces 15
Page 187 and 188:
In Eqs. [18] and [19], F0i is the e
Page 189 and 190:
where ‘‘þ’’ and ‘‘ ’
Page 191 and 192:
is, however, small for the usual co
Page 193 and 194:
solute-solvent coupling through the
Page 195 and 196:
where Z is the electrode overpotent
Page 197 and 198:
energy surfaces of ET. 33,50-56 It
Page 199 and 200:
This result indicates a fundamental
Page 201 and 202:
βF i (X ) 40 20 0 −20 1 2 βλ 1
Page 203 and 204:
Table 1 Main Features of the Two-Pa
Page 205 and 206:
and Hss ¼ U rep ss 1 2 X j;k ðmj
Page 207 and 208:
λ i /eV 4 3 2 1 0 0 10 20 30 40 Bo
Page 209 and 210:
the width/Stokes shift relation (Eq
Page 211 and 212:
Table 3 Mapping of the Q Model on S
Page 213 and 214:
This situation is of course not sat
Page 215 and 216:
F ± (Y ad )/λ I 0.8 0.4 0 −0.4
Page 217 and 218:
it by choosing the GMH basis set 7
Page 219 and 220:
Anharmonic higher order terms gain
Page 221 and 222:
of the radiation is the perturbatio
Page 223 and 224:
individual vibrational excitations
Page 225 and 226:
m 12 /D 6 5.5 5 4.5 4 3.5 17 18 19
Page 227 and 228:
In Eq. [144], the coordinates Y km
Page 229 and 230:
ε/M −1 cm −1 4000 2000 0 analy
Page 231 and 232:
βσ 2 /10 3 cm −1 14 12 10 8 Opt
Page 233 and 234:
0.2 0.1 0 12 16 20 24 28 32 The app
Page 235 and 236:
References 207 7. M. D. Newton, Adv
Page 237 and 238:
References 209 59. R. Kubo and Y. T
show all

Reviews in Computational Chemistry Volume 18

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?