Title of article
Using Soft Consensus Clustering for Combining Multiple Clusterings of Chemical Structures
Author/Authors
Saeed, Faisal Sanhan Community College - Information Technology Department, Yemen , Saeed, Faisal Universiti Teknologi Malaysia - Faculty of Computing, Malaysia , Salim, Naomie Universiti Teknologi Malaysia - Faculty of Computer Science and Information Systems, Malaysia
From page
9
To page
11
Abstract
The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA).
Keywords
Consensus clustering , graph partitioning , molecular datasets , soft clustering
Journal title
Jurnal Teknologi :F
Journal title
Jurnal Teknologi :F
Record number
2715934
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