Adaptive Cumulative Voting-Based Aggregation Algorithm for Combining Multiple Clusterings of Chemical Structures

Many consensus clustering methods have been studied and applied in many areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, Adaptive Cumulative Voting-based Aggregation Algorithm (A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of clustering to separate active from inactive molecules in each cluster and the results were compared to the Ward's method. The chemical dataset MDL Drug Data Report (MDDR) database was used. Experiments suggest that the adaptive cumulative voting-based consensus method can efficiently improve the effectiveness of combining multiple clustering of chemical structures.