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Table 4 Extracted association rules

From: An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety

N

Antecedent

Consequent

Support

Confidence

Lift

1

{}

= > {Light_Condition = Day}

0.850

0.850

1.000

2

{Road_Geometry = Horizontal}

= > {Light_Condition = Day}

0.300

1.000

1.176

3

{Drive_Age= [21–27]}

= > {Light_Condition = Day}

0.300

1.000

1.176

4

{Day = Monday}

= > {Light_Condition = Day}

0.300

1.000

1.176

5

{Road_Condition = Unpaved Road}

= > {Light_Condition = Day}

0.300

0.857

1.008

6

{Causes = Speed}

= > {Road_age= [11–20]}

0.300

0.857

1.905

7

{Victim_Age= [2–5]}

= > {Light_Condition = Day}

0.300

0.857

1.008

8

{Number_of_injuries = 1}

= > {Light_Condition = Day}

0.350

1.000

1.176

9

{Vehicle_Age = <5}

= > {Light_Condition = Day}

0.300

0.857

1.008

10

{Time= [6–12]}

= > {Light_Condition = Day}

0.350

1.000

1.176

11

{Road_age= > 20}

= > {Season = Summer}

0.300

0.750

1.364

12

{Road_age= > 20}

= > {Light_Condition = Day}

0.350

0.875

1.029

13

{Accident_Type = Fatal}

= > {Weather_Condition = Clear}

0.300

0.750

1.364

14

{Accident_Type = Fatal}

= > {Drive_Sex = M}

0.400

1.000

1.818

15

{Drive_Sex = M}

= > {Accident_Type = Fatal}

0.400

0.727

1.818

16

{Accident_Type = Fatal}

= > {Light_Condition = Day}

0.350

0.875

1.029

17

{Vehicle_Type = Car}

= > {Light_Condition = Day}

0.350

0.778

0.915

18

{Road_age= [11–20]}

= > {Light_Condition = Day}

0.350

0.778

0.915

19

{Drive_Sex = F}

= > {Accident_Type = Injury}

0.450

1.000

2.000

20

{Accident_Type = Injury}

= > {Drive_Sex = F}

0.450

0.900

2.000

21

{Drive_Sex = F}

= > {Light_Condition = Day}

0.400

0.889

1.046

22

{Victim_Age= > 5}

= > {Light_Condition = Day}

0.400

0.889

1.046

23

{Time= [12–18]}

= > {Season = Summer}

0.450

0.900

1.636

24

{Season = Summer}

= > {Time= [12–18]}

0.450

0.818

1.636

25

{Time= [12–18]}

= > {Light_Condition = Day}

0.500

1.000

1.176

26

{Number_of_Injuries= [2–5]}

= > {Road_Geometry = Alignment}

0.350

0.700

1.273

27

{Number_of_Injuries= [2–5]}

= > {Light_Condition = Day}

0.350

0.700

0.824

...

…

…

…

…

…

53

{Time= [12–18] Season = Summer}

= > {Light_Condition = Day}

0.450

1.000

1.176

54

{Light_Condition = Day Time= [12–18]}

= > {Season = Summer}

0.450

0.900

1.636

55

{Light_Condition = Day Season = Summer}

= > {Time= [12–18]}

0.450

0.818

1.636

56

{Season = Summer Number_of_Deaths= [2–5]}

= > {Light_Condition = Day}

0.300

1.000

1.176

57

{Light_Condition = Day Number_of_Deaths= [25]}

= > {Season = Summer}

0.300

0.750

1.364

58

{Weather_Condition = Clear Road_Geometry = Alignment}

= > {Light_Condition = Day}

0.300

0.857

1.008

59

{Light_Condition = Day Road_Geometry = Alignment}

= > {Weather_Condition = Clear}

0.300

0.750

1.364

60

{Weather_Condition = Clear Season = Summer}

= > {Light_Condition = Day}

0.350

1.000

1.176

61

{Light_Condition = Day Weather_Condition = Clear}

= > {Season = Summer}

0.350

0.700

1.273

62

{Drive_Sex = M Season = Summer}

= > {Light_Condition = Day}

0.300

1.000

1.176

63

{Drive_Sex = M Weather_Condition = Clear}

= > {Light_Condition = Day}

0.300

1.000

1.176

64

{Accident_Type = Fatal Driver_Sex = M Weather_Condition = Clear}

= > {Light_Condition = Day}

0.300

1.000

1.176

65

{Accident_Type = Fatal Light_Condition = Day Weather_Condition = Clear}

= > {Drive_Sex = M}

0.300

1.000

1.818

66

{Accident_Type = Fatal Driver_Sex = M Light_Condition = Day}

= > {Weather_Condition = Clear}

0.300

0.857

1.558

67

{Driver_Sex = M Light_Condition = Day Weather_Condition = Clear}

= > {Accident_Type = Fatal}

0.300

1.000

2.500