title: Combining multiple classifications of chemical structures using consensus clustering.
abstract: Consensus clustering involves combining multiple clusterings of the same set of objects to achieve a single clustering that will, hopefully, provide a better picture of the groupings that are present in a dataset. This Letter reports the use of consensus clustering methods on sets of chemical compounds represented by 2D fingerprints. Experiments with DUD, IDAlert, MDDR and MUV data suggests that consensus methods are unlikely to result in significant improvements in clustering effectiveness as compared to the use of a single clustering method.
authors.affiliation: Information School, University of Sheffield, 211 Portobello Street, Sheffield S1 4DP, UK.
authors.forename: Chia-Wei
authors.initials: CW
authors.lastname: Chu
authors.forename: John D
authors.initials: JD
authors.lastname: Holliday
authors.forename: Peter
authors.initials: P
authors.lastname: Willett
mesh_terms: D016000:Cluster Analysis; D062313:Databases, Pharmaceutical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000032:analysis* / Q000737:chemistry*
chemical_list: D004364:Pharmaceutical Preparations