Paper Info
Title
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.
Year
DOI
Venue
2013
10.1007/978-3-642-36543-0_19
ACIIDS
Keywords
Field
DocType
activity class,multiple clusterings,voting-based consensus method,cumulative voting-based aggregation algorithm,chemical structure,consensus clustering,individual clusterings,chemical structures clusterings,consensus scoring,mdl drug data report
Data mining,Computer science,Robustness (computer science),Consensus clustering,Artificial intelligence,Virtual screening,Cheminformatics,Cumulative voting,Voting,Algorithm,Sensor fusion,Machine learning,Distance measures
Conference
Volume
ISSN
Citations 
7803
0302-9743
0
PageRank 
References 
Authors
0.34
19
4
Name
Order
Citations
PageRank
Faisal Saeed13713.24
Naomie Salim242448.23
Ammar Abdo3627.89
Hamza Hentabli4164.36