title: Consensus methods for combining multiple clusterings of chemical structures.
abstract: The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering.
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Malaysia. alsamet.faisal@gmail.com
authors.forename: Faisal
authors.initials: F
authors.lastname: Saeed
authors.forename: Naomie
authors.initials: N
authors.lastname: Salim
authors.forename: Ammar
authors.initials: A
authors.lastname: Abdo
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D062313:Databases, Pharmaceutical; D048088:Informatics / Q000379:methods*