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Table 4 Clustering performance obtained by the single controlled vocabulary and the multi-view approach

From: Gene prioritization and clustering by multi-view text mining

Single CV

RI

NMI

Integration (9 CVs)

RI

NMI

LDDB

0.7586 ± 0.0032

0.5290 ± 0.0032

Ward linkage

0.8236 ± 0

0.6015 ± 0

OMIM

0.7216 ± 0.0009

0.4606 ± 0.0028

EACAL

0.7741 ± 0.0041

0.5542 ± 0.0068

Uniprot

0.7130 ± 0.0013

0.4333 ± 0.0091

OKKC(μ min = 0)

0.7641 ± 0.0078

0.5395 ± 0.0147

eVOC

0.7015 ± 0.0043

0.4280 ± 0.0079

MCLA

0.7596 ± 0.0021

0.5268 ± 0.0087

MPO

0.7064 ± 0.0016

0.4301 ± 0.0049

QMI

0.7458 ± 0.0039

0.5084 ± 0.0063

MeSH

0.6673 ± 0.0055

0.3547 ± 0.0097

OKKC(μ min = 1/N)

0.7314 ± 0.0054

0.4723 ± 0.0097

SNOMED

0.6539 ± 0.0063

0.3259 ± 0.0096

AdacVote

0.7300 ± 0.0045

0.4093 ± 0.0100

GO

0.6525 ± 0.0063

0.3254 ± 0.0092

CSPA

0.7011 ± 0.0065

0.4479 ± 0.0097

KO

0.5900 ± 0.0014

0.1928 ± 0.0042

Complete linkage

0.6874 ± 0

0.3028 ± 0

   

Average linkage

0.6722 ± 0

0.2590 ± 0

   

HGPA

0.6245 ± 0.0035

0.3015 ± 0.0071

   

Single linkage

0.5960 ± 0

0.1078 ± 0

Integration (9 LSI)

RI

NMI

Integration (35 subset CVs)

RI

NMI

Ward linkage

0.7991 ± 0

0.5997 ± 0

Ward linkage

0.8172 ± 0

0.5890 ± 0

OKKC(μ min = 0)

0.7501 ± 0.0071

0.5220 ± 0.0104

OKKC(μ min = 0)

0.7947 ± 0.0052

0.5732 ± 0.0096

EACAL

0.7511 ± 0.0037

0.5232 ± 0.0075

EACAL

0.7815 ± 0.0064

0.5701 ± 0.0082

  1. For integration of 9 LSI and 35 subset CVs, only the best three results are shown.