By Kenny B. Lipkowitz (Editor), Donald B. Boyd (Editor)
This quantity, like these ahead of it, good points chapters through specialists in a number of fields of computational chemistry. issues coated 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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It does not rely on vector operations but builds up the cluster summaries on the basis of a distance function that obeys the triangle inequality, an operation that is more CPU demanding than operations in coordinate space. 18 Clustering Methods and Their Uses in Computational Chemistry Nearest-neighbor nonhierarchical methods have received much attention in the chemical community because of their fast processing speeds and ease of implementation. The comparative studies outlined in the next section (Comparative Studies on Chemical Data Sets) led to the widespread adoption of the Jarvis–Patrick nearest-neighbor method for clustering large chemical data sets.
6. Start again with the ﬁrst data vector, and repeat steps 2–5 for a given number of cycles. The iterative adjustment of weight vectors is similar to the iterative reﬁnement of k-means clustering to derive cluster centroids. The main difference is that adjustment affects neighboring weight vectors at the same time. Kohonen mapping requires O(Nmn) time and O (N ) space, where m is the number of cycles and n the number of neurons. Other Nonhierarchical Methods We have delineated the main categories of clustering methods applicable to chemical applications above.
The methods must be able to handle large data sets of high-dimensional data. For small, low-dimensional data sets, most clustering methods are applicable, and descriptions in the standard texts and implementations available in standard statistical software packages10,11 sufﬁce. 18 The overall process of clustering involves the following steps: 1. 2. 3. 4. Generate appropriate descriptors for each compound in the data set. Select an appropriate similarity measure. Use an appropriate clustering method to cluster the data set.