Table 2 Inferring quality from clustering methods
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 |