Supplementary Material: HAUS-rules

This website contains supplementary material to the paper ESWA-D-21-00806 submitted to be taken into account for possible publication in Expert Systems With Applications.

Summary:

  1. Results obtained by the analyzed algorithms on each of the datasets
  2. Dataset for the biological problem

 

Results obtained by the analyzed algorithms on each of the datasets

Synthetic 1

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 343 3.31 (0.88) 0.58 (0.07) 0.85 (0.07) 0.97 (0.04) -0.04 (0.06) -0.92 (3.86) 9324.64 (2880.88) -372.99 (509.62)
HUSRM 1566 10.89 (0.34) 0.01 (0.01) 0.84 (0.1) 2.71 (0.32) 0.76 (0.14) 0.84 (0.11) 129.6 (18.08) 98.3 (23.59)
HAUS-rules 17 3.53 (1.1) 0.31 (0.18) 0.95 (0.07) 1.09 (0.12) 0.74 (0.24) 0.86 (0.21) 5650.87 (3949.28) 4181.64 (614.85)

Synthetic 2

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 322 3.26 (0.84) 0.58 (0.07) 0.84 (0.07) 0.96 (0.04) -0.04 (0.06) -0.77 (1.34) 18554.7 (5800.96) -742.2 (1024.53)
HUSRM 884 10.85 (0.39) 0.02 (0.01) 0.76 (0.06) 1.67 (0.17) 0.55 (0.13) 0.58 (0.13) 493.58 (38.56) 271.47 (63.03)
HAUS-rules 11 3.28 (1.14) 0.31 (0.21) 0.92 (0.1) 1.15 (0.18) 0.59 (0.29) 0.63 (0.25) 11526.16 (9086.71) 6800.43 (1943.6)

Synthetic 3

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 317 3.25 (0.84) 0.58 (0.07) 0.84 (0.07) 0.97 (0.04) -0.04 (0.06) -0.76 (1.45) 46196.01 (14527.09) -1847.84 (2544.18)
HUSRM 683 10.87 (0.36) 0.02 (0.01) 0.76 (0.05) 1.69 (0.17) 0.56 (0.11) 0.59 (0.11) 1147.04 (104.35) 642.34 (127.0)
HAUS-rules 12 3.25 (1.02) 0.32 (0.16) 0.95 (0.05) 1.07 (0.04) 0.63 (0.28) 0.63 (0.25) 26723.55 (14732.57) 16835.83 (1035.6)

Synthetic 4

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 312 3.24 (0.84) 0.58 (0.07) 0.84 (0.07) 0.96 (0.04) -0.04 (0.06) -0.75 (1.41) 64400.52 (20438.25) -2576.02 (3587.99)
HUSRM 30214 10.88 (0.37) 0.01 (0.01) 0.84 (0.11) 2.07 (0.66) 0.71 (0.22) 0.73 (0.22) 1038.72 (92.04) 737.49 (220.68)
HAUS-rules 12 3.42 (0.96) 0.32 (0.21) 0.96 (0.03) 1.06 (0.04) 0.76 (0.31) 0.75 (0.27) 41202.66 (28803.25) 31314.02 (2665.73)

Synthetic 5

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 316 3.26 (0.84) 0.58 (0.07) 0.84 (0.07) 0.97 (0.04) -0.04 (0.06) -0.77 (1.52) 92291.01 (29004.59) -3691.64 (5064.01)
HUSRM 13309 10.82 (0.45) 0.01 (0.01) 0.84 (0.1) 1.7 (0.39) 0.66 (0.2) 0.66 (0.21) 865.8 (92.44) 571.43 (172.21)
HAUS-rules 13 3.16 (1.03) 0.38 (0.22) 0.95 (0.04) 1.06 (0.05) 0.66 (0.32) 0.63 (0.37) 70072.7 (51126.45) 46247.98 (7010.09)

Bible

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 178 3.8 (0.99) 0.03 (0.05) 0.79 (0.07) 5.12 (10.92) 0.48 (0.29) 0.51 (0.35) 9841.06 (17278.23) 4723.71 (2939.89)
HUSRM 179 5.1 (1.43) 0.02 (0.05) 0.83 (0.05) 9.5 (16.85) 0.66 (0.21) 0.71 (0.29) 6000.85 (16337.95) 3960.56 (2319.55)
HAUS-rules 17 3.59 (1.15) 0.01 (0.02) 0.71 (0.22) 11.49 (14.21) 0.68 (0.24) 0.76 (0.32) 2936.12 (3908.47) 1996.56 (694.15)

BMS

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 1 2.0 (0.0) 0.01 (0.0) 0.79 (0.0) 12.9 (0.0) 0.78 (0.0) 0.98 (0.0) 9044.0 (0.0) 7054.32 (0.0)
HUSRM 50408 10.99 (0.13) 0.01 (0.01) 1.0 (0.02) 63.39 (0.69) 1.0 (0.02) 0.0 (0.0) 699.3 (16.38) 699.3 (16.38)
HAUS-rules 8 2.5 (0.71) 0.01 (0.01) 0.71 (0.24) 12.39 (4.4) 0.69 (0.26) 0.95 (0.06) 10326.04 (6133.35) 7124.97 (1885.01)

FIFA

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules -                
HUSRM 2959 6.82 (0.44) 0.15 (0.02) 0.74 (0.03) 2.13 (0.09) 0.6 (0.04) 0.79 (0.03) 84593.32 (7936.9) 50755.99 (5345.0)
HAUS-rules 43 4.17 (1.87) 0.08 (0.1) 0.83 (0.06) 53.93 (135.05) 0.79 (0.1) 0.94 (0.16) 46555.99 (63311.72) 36779.23 (38653.17)

Kosarak

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 66 3.46 (0.95) 0.03 (0.05) 0.87 (0.09) 7.84 (4.96) 0.82 (0.11) 0.93 (0.12) 4314.42 (6870.69) 3537.82 (5036.08)
HUSRM 1260 9.0 (0.07) 0.01 (0.01) 0.72 (0.01) 1.57 (0.01) 0.48 (0.01) 0.0 (0.0) 147.92 (3.32) 71.0 (1.58)
HAUS-rules 4 3.0 (0.71) 0.13 (0.12) 0.96 (0.05) 1.58 (0.07) 0.89 (0.11) 0.92 (0.08) 19975.05 (19581.73) 17777.79 (13644.25)

SIGN

Algorithm #R Av_Amp (s) Av_Sup (s) Av_Conf (s) Av_Lift (s) Av_CF (s) Av_YulesQ (s) aveUti (s) CF*aveUti (s)
CMRules 1279 3.89 (1.01) 0.48 (0.08) 0.84 (0.06) 0.96 (0.06) -0.04 (0.1) -0.59 (0.8) 5904.47 (1803.35) -236.18 (466.58)
HUSRM 181 5.3 (1.24) 0.45 (0.1) 0.84 (0.05) 0.95 (0.03) -0.06 (0.03) -0.65 (0.51) 7222.02 (2031.25) -433.32 (177.74)
HAUS-rules 13 3.31 (1.14) 0.33 (0.19) 0.92 (0.14) 1.03 (0.08) 0.45 (0.35) 0.29 (0.7) 4900.59 (2922.91) 2205.27 (1043.85)

 

Dataset for the biological problem

The dataset used to extract relevant biological information from a longitudinal intervention in humans can be downloaded by clicking here.

The original dataset was downloaded from the public repository GEO with identifier GSE77962.