Table 3 Effectiveness of clustering of MDDR dataset using F-Measure: ECFP_4 Fingerprint
From: Voting-based consensus clustering for combining multiple clusterings of chemical structures
Clustering method | No. of clusters | |||||||
|---|---|---|---|---|---|---|---|---|
500 | 600 | 700 | 800 | 900 | 1000 | |||
Consensus clustering | CVAA | Correlation | 33.58 | 29.81 | 24.44 | 20.09 | 18.41 | 17.43 |
Cosine | 34.75 | 31.32 | 24.97 | 20.26 | 18.46 | 17.73 | ||
Euclidean | 25.43 | 23.34 | 20.51 | 19.13 | 16.47 | 14.64 | ||
Hamming | 25.48 | 24.04 | 20.23 | 19.62 | 17.31 | 14.73 | ||
Jaccard | 35.71 | 33.17 | 28.66 | 21.8 | 19.63 | 18.86 | ||
Manhattan | 25.41 | 23.98 | 20.30 | 19.53 | 17.25 | 14.65 | ||
CSPA | Correlation | 5.53 | 4.88 | 4.23 | 3.85 | 3.6 | 3.18 | |
Cosine | 5.43 | 4.88 | 4.28 | 3.91 | 3.55 | 3.10 | ||
Euclidean | 5.47 | 4.87 | 4.17 | 3.79 | 3.53 | 3.33 | ||
Hamming | 5.45 | 4.82 | 4.23 | 3.87 | 3.58 | 3.19 | ||
Jaccard | 5.51 | 4.99 | 4.25 | 3.99 | 3.62 | 3.20 | ||
Manhattan | 5.44 | 4.85 | 4.23 | 3.89 | 3.62 | 3.20 | ||
HGPA | Correlation | 7.01 | 6.2 | 5.21 | 4.5 | 4.16 | 3.68 | |
Cosine | 6.83 | 5.95 | 5.29 | 4.47 | 4.21 | 3.93 | ||
Euclidean | 7.29 | 5.82 | 5.29 | 4.39 | 4.48 | 3.94 | ||
Hamming | 7.01 | 5.83 | 5.29 | 4.50 | 4.37 | 3.69 | ||
Jaccard | 6.87 | 5.91 | 5.31 | 4.81 | 4.80 | 3.66 | ||
Manhattan | 7.81 | 5.17 | 5.38 | 4.61 | 4.66 | 3.68 | ||
Individual clustering | Ward's method | 11.61 | 10.71 | 9.04 | 8.29 | 7.64 | 7.02 | |