Table 4 Effectiveness of clustering of MDDR dataset using QPI: ALOGP 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 | 43.84 | 47.38 | 48.72 | 50.70 | 53.41 | 54.06 |
Cosine | 45.60 | 46.08 | 47.56 | 50.46 | 53.79 | 54.50 | ||
Euclidean | 44.43 | 45.54 | 47.95 | 48.65 | 52.68 | 54.86 | ||
Hamming | 53.13 | 56.08 | 59.07 | 60.58 | 64.02 | 67.76 | ||
Jaccard | 57.86 | 60.62 | 64.07 | 66.49 | 70.68 | 73.53 | ||
Manhattan | 56.01 | 58.10 | 60.99 | 61.86 | 64.56 | 65.97 | ||
CSPA | Correlation | 46.81 | 50.04 | 51.72 | 51.78 | 54.23 | 56.36 | |
Cosine | 46.04 | 49.49 | 51.42 | 52.11 | 54.48 | 55.92 | ||
Euclidean | 46.20 | 49.86 | 51.05 | 51.88 | 54.36 | 56.33 | ||
Hamming | 54.67 | 58.50 | 60.27 | 61.78 | 62.33 | 65.66 | ||
Jaccard | 55.03 | 59.13 | 60.84 | 61.03 | 63.73 | 67.44 | ||
Manhattan | 55.08 | 59.00 | 59.10 | 60.84 | 61.78 | 64.61 | ||
HGPA | Correlation | 47.59 | 49.51 | 52.39 | 54.45 | 56.86 | 58.56 | |
Cosine | 45.58 | 48.44 | 52.78 | 54.42 | 56.36 | 58.70 | ||
Euclidean | 46.92 | 51.41 | 53.20 | 54.75 | 57.00 | 58.97 | ||
Hamming | 55.24 | 58.48 | 60.30 | 63.99 | 68.21 | 69.22 | ||
Jaccard | 55.71 | 59.89 | 64.10 | 65.15 | 70.48 | 71.60 | ||
Manhattan | 54.84 | 58.98 | 62.73 | 63.58 | 65.85 | 69.97 | ||
Individual clustering | Ward's method | 52.33 | 54.86 | 56.90 | 59.00 | 61.33 | 63.17 | |