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Clustering Algorithms and Pattern Recognition Techniques

There is no correct method of performing cluster analysis and a large number of algorithms have been devised from which one must choose the most appropriate approach. There can also be a wide variation in the efficiency of the various cluster algorithms, which may be an important consideration if the data set is large. [Pg.491]

A cluster analysis requires a measure of the similarity (or dissimilarity) between pairs of objects. When comparing conformations, the RMSD would be an obvious measure to use. [Pg.491]

Alternatively, the distance between two conformations can be measured in terms of their torsion angles. Here, there may be more than one way in which the distance can be calculated. The Euclidean distance between two conformations would be calculated using. [Pg.492]

29 Euclidean and Hamming distance measures of torsional similarity [Pg.492]

9 30 Ribose phosphate fragment used to extract data from Cambridge Structural Database and eight sets of torsion angle values for tj and T2 [Pg.493]


See other pages where Clustering Algorithms and Pattern Recognition Techniques is mentioned: [Pg.507]    [Pg.491]   


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