Skip to main content

Table 5 Effectiveness of clustering of MDDR dataset using QPI: 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 74.86 78.02 82.39 84.16 85.71 87.04
Cosine 74.79 78.12 81.85 84.78 85.91 87.18
Euclidean 71.04 74.92 78.41 81.91 84.47 86.80
Hamming 70.99 74.36 78.47 81.68 84.24 86.28
Jaccard 83.48 87.01 88.72 90.98 90.67 92.05
Manhattan 70.74 74.26 78.52 81.74 84.12 86.09
CSPA Correlation 70.58 73.29 74.86 76.86 79.17 82.03
Cosine 71.23 71.85 76.43 76.55 78.06 81.21
Euclidean 65.33 67.09 72.49 72.73 74.50 78.75
Hamming 64.68 66.82 69.88 71.25 74.17 76.64
Jaccard 69.91 71.73 74.20 76.01 77.72 79.26
Manhattan 63.07 65.77 68.83 71.50 74.06 77.33
HGPA Correlation 72.61 74.85 76.4 78.32 80.22 82.26
Cosine 72.06 74.25 77.21 79.54 81.02 83.31
Euclidean 70.71 72.82 75.02 76.80 80.50 82.66
Hamming 69.45 72.21 74.08 77.71 79.67 82.36
Jaccard 67.88 70.58 73.93 76.56 77.65 79.67
Manhattan 72.74 72.14 75.68 77.94 81.42 82.97
Individual clustering Ward's method   75.83 79.88 83.34 84.25 86.49 88.25
\