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Clustering files of chemical structures using the fuzzy k-means clustering method
John D Holliday1, Sarah L Rodgers, Peter Willett
1Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K.
Abstract:
This paper evaluates the use of the fuzzy k-means clustering method for the clustering of files of 2D chemical structures. Simulated property prediction experiments with the Starlist file of logP values demonstrate that use of the fuzzy k-means method can, in some cases, yield results that are superior to those obtained with the conventional k-means method and with Ward's clustering method. Clustering of several small sets of agrochemical compounds demonstrate the ability of the fuzzy k-means method to highlight multicluster membership and to identify outlier compounds, although the former can be difficult to interpret in some cases.
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