Asymmetric attribute in data mining

DATA MINING

1- Consider the traffic accident data set shown in Table below.

Traffic accident data set.

Weather Condition

Driver’s

Condition

Traffic Violation

Seat Belt

Crash

Severity

Good

Bad

Good

Bad

Bad

Bad

Bad

Good

Good

Bad

Good

Bad

Alcohol-impaired

Sober

Sober

Alcohol-impaired

Alcohol-impaired

Alcohol-impaired

Alcohol-impaired

Sober

Alcohol-impaired

Sober

Alcohol-impaired

Sober

Exceed speed limit

None

Disobey stop sign

Exceed speed limit

Disobey traffic signal

Disobey stop sign

None

Disobey traffic signal

None

None

Exceed speed limit

Disobey stop sign

No

Yes

No

Yes

No

Yes

Yes

Yes

No

Yes

Yes

Yes

Major

Minor

Minor

Major

Major

Minor

Major

Minor

Minor

Major

Major

Minor

a. Show a binarized version of the data set.

Answer:

b. What is the maximum width of each transaction in the binarized data?

Answer:

c. Assuming that support threshold is 30%, how many candidate and frequent item sets will be generated?

2- Consider the data set shown in Table below. The first attribute is continuous, while the remaining two attributes are asymmetric binary. A rule is considered to be strong if its support exceeds 15% and its confidence exceeds 60%. The data given in Table below supports the following two strong rules:

(i) {(1 ≤ A ≤ 2), B = 1} → {C = 1}

(ii) {(5 ≤ A ≤ 8), B = 1} → {C = 1}

A

B

C

1

2

3

4

5

6

7

8

9

10

11

12

1

1

1

1

1

0

0

1

0

0

0

0

1

1

0

0

1

1

0

1

0

0

0

1

a. Compute the support and confidence for both rules.

Answer:

S ({(1 ≤ A ≤ 2), B = 1} → {C = 1}) =

C ({(1 ≤ A ≤ 2), B = 1} → {C = 0}) =

S ({(5 ≤ A ≤ 9), B = 1} → {C = 1}) =

C ({(5 ≤ A ≤ 9), B = 1} → {C = 1}) =

3. Consider the data set shown in Table below. Suppose we are interested in extracting the following association rule:

{α1 ≤ Age ≤ α2, Play Piano = Yes} → {Enjoy Classical Music = Yes}

Age

Play Piano

Enjoy Classical Music

9

11

14

17

19

21

25

29

33

39

41

47

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Yes

Yes

No

No

Yes

Yes

No

No

Yes

No

No

No

No

Yes

Yes

Yes

To handle the continuous attribute, we apply the equal-frequency approach with 3, 4, and 6 intervals. Categorical attributes are handled by introducing as many new asymmetric binary attributes as the number of categorical values. Assume that the support threshold is 10% and the confidence threshold is 70%.

(a) Suppose we discretize the Age attribute into 3 equal-frequency intervals. Find a pair of values for α1 and α2 that satisfy the minimum support and minimum confidence requirements.

Answer:

(b) Repeat part (a) by discretizing the Age attribute into 4 equal-frequency intervals. Compare the extracted rules against the ones you had obtained in part (a).

Answer:

(c) Repeat part (a) by discretizing the Age attribute into 6 equal-frequency intervals. Compare the extracted rules against the ones you had obtained in part (a).

Answer:

4. For each of the sequence w = below, determine whether they are subsequences of the following data sequence:

<{A, B}{C, D}{A, B}{C, D}{A, B}{C, D}>

subjected to the following timing constraints:

mingap = 0 (interval between last event in ei and first event in ei+1 is > 0)

maxgap = 2 (interval between first event in ei and last event in ei+1 is ≤ 2)

maxspan = 6 (interval between first event in e1 and last event in elast is ≤ 6)

ws = 1 (time between first and last events in ei is ≤ 1)

a. w = < {A}{B}{C}{D}> Answer:

b. w = < {A} {B, C, D} {A}> Answer:

c. w = < {A} {B, C, D} {A}> Answer:

d. w = < {B, C} {A, D} {B, C}> Answer:

e. w = < {A, B, C, D} {A, B, C, D}> Answer:

5. Draw all candidate subgraphs obtained from joining the pair of graphs shown in Figure below Assume the edge-growing method is used to expand the subgraphs.