Paper Info
Title
Combining multiple clusterings of chemical structures using cluster-based similarity partitioning algorithm.
Abstract
Many types of clustering techniques for chemical structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of individual clusterings. In this paper, the Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and the results were compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results, obtained by combining multiple clusterings, showed that the consensus clustering method can improve the robustness, novelty and stability of chemical structures clustering.
Year
DOI
Venue
2014
10.1504/IJCBDD.2014.058584
Int. J. Comput. Biol. Drug Des.
Keywords
Field
DocType
consensus clustering,ward s method,similarity matrix,distance measures,graph partitioning
Data mining,Fuzzy clustering,CURE data clustering algorithm,Consensus clustering,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Clustering high-dimensional data,Correlation clustering,Pattern recognition,Algorithm,Bioinformatics,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
7
1
1756-0756
Citations 
PageRank 
References 
1
0.35
11
Authors
3
Name
Order
Citations
PageRank
Faisal Saeed13713.24
Naomie Salim242448.23
Ammar Abdo3627.89