Combining multiple clusterings of chemical structures using cumulative voting-based aggregation algorithm

Abstract

The use of consensus clustering methods in chemoinformatics is motivated because of the success of consensus scoring (data fusion) in virtual screening and also because of the ability of consensus clustering to improve the robustness, novelty, consistency and stability of individual clusterings in other areas. In this paper, Cumulative Voting-based Aggregation Algorithm (CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the extent to which they clustered compounds, which belong to the same activity class, together. Then, the results were compared to other consensus clustering and Ward's methods. The MDL Drug Data Report (MDDR) database was used for experiments and the results were obtained by combining multiple clusterings that were applied using different distance measures. The experiments show that the voting-based consensus method can efficiently improve the effectiveness of chemical structures clusterings.