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Table 2 Inferring quality from clustering methods

From: A singular value decomposition approach for improved taxonomic classification of biological sequences

Algorithm/ software Rank N Min cLtlf Max cLtlf Mean cLtlf cLtlf clusters sum (∑cLtlf) cLtlf standard deviation (σ) Linnaean clusters quality (∑cLtlf/σ) Linnaean clusters quality gain (K09/K60)% cLtlf median Median clusters quality gain (K09/K60)%
AQBC-javaml K09 8 32 180 71.25 570 52.27 10.90 49.58% 42.50 26.87%
  K60 8 0 220 64.38 515 70.64 7.29   33.50  
EM-weka K09 8 40 120 70.12 561 31.53 17.79 48.99% 57.00 1.79%
  K60 8 16 160 70.25 562 47.06 11.94   56.00  
Kmeans-weka K09 8 30 180 69.38 555 46.70 11.88 9.26% 61.50 -2.38%
  K60 8 16 180 69.88 559 51.39 10.88   63.00  
Kmeans-R K09 8 40 140 71.62 573 34.48 16.62 9.21% 62.00 6.90%
  K60 8 26 140 71.75 574 37.72 15.22   58.00  
K-Medoids-R K09 8 24 160 70.12 561 44.37 12.64 15.92% 60.00 13.21%
  K60 8 26 180 68.50 548 50.24 10.91   53.00  
MDBC-weka K09 8 30 180 69.38 555 46.70 11.88 9.26% 61.50 -2.38%
  K60 8 16 180 69.88 559 51.39 10.88   63.00  
ASAP-in house K09 8 13 225 70.25 562 67.68 8.30 27.51% 52.00 197.14%
  K60 8 13 243 69.12 553 84.92 6.51   17.50  
  1. All evaluated partitioning's algorithms showed improved performance considering the Linnaean clusters quality when used the optimized distance matrix created by the better kdc parameters tested.
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