title: Clustering files of chemical structures using the fuzzy k-means clustering method.
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.
authors.affiliation: Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K.
authors.forename: John D
authors.initials: JD
authors.lastname: Holliday
authors.forename: Sarah L
authors.initials: SL
authors.lastname: Rodgers
authors.forename: Peter
authors.initials: P
authors.lastname: Willett
authors.forename: Min-You
authors.initials: MY
authors.lastname: Chen
authors.forename: Mahdi
authors.initials: M
authors.lastname: Mahfouf
authors.forename: Kevin
authors.initials: K
authors.lastname: Lawson
authors.forename: Graham
authors.initials: G
authors.lastname: Mullier