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Table 5

Clusters identified by Lacerda et al. (2014, Col. 1), using the Lacerda et al. (2014) sample with updates (this work, Col. 2), and usingall targets (this work, Col. 3).

Lacerda et al. (2014) Original sample All targets
Mathematica/FindClusters R/MClust, VVI R/MClust, VVI

pV S ID pV S ID pV S ID
~0.05 ~10 DN 0.047 ± 0.008 9.7 ± 4.6 DN 0.046 ± 0.008 10.0 ± 4.5 DN
~0.15 ~35 BR 0.125 ± 0.071 29.1 ± 12.3 BR 0.121 ± 0.056 26.7 ± 13.6 BR
>0.30 ~0 BN 0.564 ± 0.239 4.6 ± 5.8 BN 0.538 ± 0.221 9.5 ± 13.0 BN

Notes. Clusters are characterized here by their mean geometric albedo (pV) and spectralslope (S′, %/1000 Å), and the standard deviations using an ellipsoidal varying volume and shape data model. In the second row we also list the tools used to identify the clusters.

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