Adaptive cumulative voting-based aggregation algorithm for combining multiple clusterings of chemical structures

Authors:
Faisal Saeed;Naomie Salim;Ammar Abdo;Hamza Hentabli
Affiliations:
Faculty of Computing, Universiti Teknologi Malaysia, Malaysia, Information Technology Department, Sanhan Community College, Sana'a, Yemen;Faculty of Computing, Universiti Teknologi Malaysia, Malaysia;Computer Science Department, Hodeidah University, Hodeidah, Yemen, LIFL UMR CNRS 8022, Universite' Lille 1 and INRIA Lille Nord Europe, Villeneuve d'Ascq cedex, France;Faculty of Computing, Universiti Teknologi Malaysia, Malaysia
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

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Abstract

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.