Results of contrast set learning techniques
What was done using different techniques:
1. Cluster jplflight(-->C1) with xy_proj.py -->C2
2. Build decision trees using xy_dt.py
3. Use diff.py to get decisions(contrast sets) to be made for worse cluster to be better cluster.
4. Using the contrast sets generate 500 samples with gen.py. (used xomo)-->C3
5. Compare initial clusters to newly generated data.
6. Represent results as in fig 9 of http://menzies.us/pdf/12gense.pdf .
Techniques:
T0: asIs
T2 =C1+C3
T3 = C2+C3
Flight data
Techniques:
T0: asIs
T2 =C1+C3
T3 = C2+C3
Flight data
Techniques -effort -months -defects -risks #
T0 m 43 74 14 9 #
T2 m 0 3 0 1 #
T3 m 0 4 0 0 #
T0 q 32 17 21 26 #
T2 q 0 0 1 2 #
T3 q 0 0 1 2 #
T0 w 100 100 100 100 #
T2 w 2 7 25 15 #
T3 w 2 7 21 13 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 4340.4 0.2 #
Ground data
Techniques -effort -months -defects -risks #
T0 m 43 74 15 10 #
T2 m 0 3 0 1 #
T3 m 0 3 0 0 #
T0 q 32 17 21 27 #
T2 q 0 0 1 3 #
T3 q 0 0 1 3 #
T0 w 100 100 100 100 #
T2 w 1 7 18 17 #
T3 w 1 6 16 15 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 4340.4 0.2 #
Osp data
Techniques -effort -months -defects -risks #
T0 m 43 74 12 0 #
T2 m 0 4 0 12 #
T3 m 0 4 0 12 #
T0 q 32 18 19 19 #
T2 q 0 0 1 9 #
T3 q 0 0 0 8 #
T0 w 100 100 100 100 #
T2 w 1 6 24 33 #
T3 w 1 6 20 33 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 6021.0 0.2 #
Osp2 data
Techniques -effort -months -defects -risks #
T0 m 43 74 14 3 #
T2 m 0 4 0 0 #
T3 m 0 4 0 0 #
T0 q 32 18 21 22 #
T2 q 0 0 0 2 #
T3 q 0 0 0 2 #
T0 w 100 100 100 100 #
T2 w 1 6 14 14 #
T3 w 1 6 11 14 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 6021.0 0.2 #
