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Finding Interesting Associations without Support Pruning

Date post: 12-Nov-2014
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Page 1: Finding Interesting Associations without Support Pruning
Page 2: Finding Interesting Associations without Support Pruning
Page 3: Finding Interesting Associations without Support Pruning
Page 4: Finding Interesting Associations without Support Pruning
Page 5: Finding Interesting Associations without Support Pruning
Page 6: Finding Interesting Associations without Support Pruning
Page 7: Finding Interesting Associations without Support Pruning
Page 8: Finding Interesting Associations without Support Pruning
Page 9: Finding Interesting Associations without Support Pruning
Page 10: Finding Interesting Associations without Support Pruning
Page 11: Finding Interesting Associations without Support Pruning
Page 12: Finding Interesting Associations without Support Pruning

0 20 40 60 80 1000

0.5

1

1.5

2

2.5

3

3.5

4 x 107

Num

ber S

imila

r pai

rs

Similarity (%)

Histogram of Sun data set

60 65 70 75 80 85 90 95 1000

1

2

3

4

5

6

7

8

9

10 x 104

Num

ber S

imila

r pai

rs

Similarity (%)

Histogram of Sun data set

Page 13: Finding Interesting Associations without Support Pruning

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Frac

tion

of p

airs

foun

d

Similarity (%)

Performance of MH on Sun data set, f=80

k = 10 k = 20 k = 50 k = 100k = 200

1020 50 100 200 500

200

400

600

800

1000

1200

1400

1600

1800

K Value

Tota

l tim

e (s

ec)

Running time of MH on Sun data set,f=80

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Frac

tion

of p

airs

foun

d

Similarity (%)

Performance of MH on Sun data set,k=500

f = 70f = 75f = 80f = 85f = 90

70 75 80 85 901750

1800

1850

1900

1950

F Value

Tota

l tim

e (s

ec)

Running time of MH on Sun data set,k=500

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Frac

tion

of p

airs

foun

d

Similarity (%)

Performance of K−MH on Sun data set,f=80

k = 10 k = 20 k = 50 k = 100k = 200

1020 50 100 200 500100

150

200

250

K Value

Tota

l tim

e (s

ec)

Running time of K−MH on Sun data set,f=80

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Frac

tion

of p

airs

foun

d

Similarity (%)

Performance of K−MH on Sun data set,k=500

f = 70f = 75f = 80f = 85f = 90

70 75 80 85 90200

210

220

230

240

250

260

270

280

290

300

F Value

Tota

l tim

e (s

ec)

Running time of K−MH on Sun data set,k=500

Page 14: Finding Interesting Associations without Support Pruning

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Similarity (%)

Frac

tion

of p

airs

foun

d

Performance of H−LSH on Sun data set, l = 8

r = 32r = 40r = 48r = 56

32 40 48 56

60

80

100

120

140

160

180

200

220

Value of parameter r

Tota

l tim

e (s

ec)

Running time of H−LSH on Sun data set, l = 8

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Similarity (%)

Frac

tion

of p

airs

foun

d

Performance of H−LSH on Sun data set, r = 40

l = 4 l = 8 l = 16l = 32

4 8 16 32

90

100

110

120

130

140

Value of parameter l

Tota

l tim

e (s

ec)

Running time of H−LSH on Sun data set, r = 40

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Frac

tion

of p

airs

foun

d

Similarity (%)

Performance of M−LSH on Sun data set, l = 10

r = 10 r = 20 r = 50 r = 100

10 20 50 1000

50

100

150

200

250

300

350

400

450

500

Tota

l tim

e (s

ec)

Value of parameter r

Running time of M−LSH on Sun data set, l = 10

55 65 75 85 950

0.2

0.4

0.6

0.8

1

Frac

tion

of p

airs

foun

d

Similarity (%)

Performance of M−LSH on Sun data set, r = 10

l = 10 l = 20 l = 50 l = 100

10 20 50 1000

50

100

150

200

250

300

350

400

450

500

550

time

(sec

)

Value of parameter l

Running time of M−LSH on Sun data set, r = 10

Page 15: Finding Interesting Associations without Support Pruning

0.01 0.05 0.1 0.5 1 5 100

50

100

150

200

250

300

350

400

450

500

Tota

l tim

e (s

ec)

False Negative Threshold (%)

Time vs False Negatives, Similarity = 85%

MH K−MH H−LSHM−LSH

0.01 0.05 0.1 0.5 1 5 10104

105

False Negative Threshold (%)

False Positives vs False Negatives, Similarity = 85%

MH K−MH H−LSHM−LSH

0.01 0.05 0.1 0.5 10

20

40

60

80

100

120

140

160

180

200

Tota

l tim

e (s

ec)

False Negative Threshold (%)

Time vs False Negatives, Similarity = 95%

MH K−MH H−LSHM−LSH

0.01 0.05 0.1 0.5 1

104

105

Num

ber o

f Fal

se p

ositiv

es

False Negative Threshold (%)

False Positives vs False Negatives, Similarity = 95%

MH K−MH H−LSHM−LSH

Page 16: Finding Interesting Associations without Support Pruning
Page 17: Finding Interesting Associations without Support Pruning

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