Sequential superparamagnetic clustering for unbiased classification of high-dimensional chemical data.
For the clustering of chemical structures that are described by the Similog, ISIS count, and ISIS binary fingerprints, we propose a sequential superparamagnetic clustering approach. To appropriately handle nonbinary feature keys, we introduce an extension of the binary Tanimoto similarity measure. In our applications, data sets composed of structures from seven chemically distinct compound classes are evaluated and correctly clustered. The comparison, with results from leading methods, indicates the superiority of our sequential superparamagnetic clustering approach.
Ott
Thomas
T
Institute for Neuroinformatics, University/ETH Zürich, Zurich, Switzerland.
Kern
Albert
A
Schuffenhauer
Ausgar
A
Popov
Maxim
M
Acklin
Pierre
P
Jacoby
Edgar
E
Stoop
Ruedi
R