Reviews in Computational Chemistry, Volume 18 by Kenny B. Lipkowitz (Editor), Donald B. Boyd (Editor)

By Kenny B. Lipkowitz (Editor), Donald B. Boyd (Editor)

This quantity, like these sooner than it, gains chapters by way of specialists in numerous fields of computational chemistry. issues lined in quantity 18 contain molecular modeling, computer-assisted molecular layout (camd), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (qsar).

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By Kenny B. Lipkowitz (Editor), Donald B. Boyd (Editor)

This quantity, like these sooner than it, gains chapters by way of specialists in numerous fields of computational chemistry. issues lined in quantity 18 contain molecular modeling, computer-assisted molecular layout (camd), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (qsar).

Show description

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3. Choose the cluster with the largest diameter and divide it into two clusters so that the larger cluster has the smallest possible diameter. 4. Repeat step 3 for a maximum of N – 1 divisions. Nonhierarchical Methods Single-Pass Methods that cluster data on the basis of a single scan of the data set are referred to as single-pass. A proximity threshold is typically used to decide 10 Clustering Methods and Their Uses in Computational Chemistry whether a compound is assigned to an existing cluster (represented as a centroid) or if it should be used to start a new cluster.

Various models were developed and compared using the approximate weight of evidence (AWE) statistic, which estimates the Bayesian posterior probability of the clustering solution. Fraley and Raftery86 subsequently replaced AWE by the more reliable Bayesian information criterion (BIC), which enabled them to produce an EM algorithm that simultaneously yields the best model and determines the best number of clusters. One other interesting aspect of their work is that the EM algorithm is seeded with the clustering results from hierarchical agglomerative clustering.

The successive levels can be visualized using a dendrogram, as shown in Figure 1. Each level of the hierarchy represents a partitioning of the data set into a set of clusters. In contrast, if the data set is analyzed to produce a single partition of the compounds resulting in a set of clusters, the result is then nonhierarchical. Note that the term partitioning ................................................................................................................................................

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