Name
Abstract | ||
|---|---|---|
The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1021/ci300442u | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
DocType | Volume | Issue |
Journal | 53 | 5 |
ISSN | Citations | PageRank |
1549-9596 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faisal Saeed | 1 | 37 | 13.24 |
| Naomie Salim | 2 | 424 | 48.23 |
| Ammar Abdo | 3 | 62 | 7.89 |