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Table 1 Description of 30 standard datasets

From: Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Dataset

n

d

\(d^u\)

\(d^o\)

c

Australian

690

14

10

4

2

Autos

205

24

14

10

6

Banknote

1372

4

4

0

2

Biodeg

1055

41

38

3

2

Blood

748

4

4

0

2

Bupa

345

6

6

0

2

Cleve

295

13

6

7

2

Column2C

310

6

6

0

2

Column3C

310

6

6

0

3

Ecoli

336

8

5

3

8

Faults

1941

27

25

2

7

Glass

214

9

9

0

6

Haberman

306

3

3

0

2

Hayes

132

5

5

0

3

Heart

270

13

10

3

2

Hepatitis

155

19

6

13

2

ILPD

583

10

9

1

2

Ionosphere

351

34

32

2

2

Iris

150

4

4

0

3

Liver

345

6

6

0

2

Pima

768

8

8

0

2

Seeds

210

7

7

0

3

Segment

2310

19

18

1

7

Sonar

208

60

60

0

2

Tae

151

5

3

2

3

Transfusion

748

4

4

0

2

Vowel

990

13

11

2

11

Wine

178

13

13

0

3

Wisconsin

683

9

9

0

2

Yeast

1484

9

7

2

10

  1. n, number of instances; d, number of attributes; \(d^u,\) number of numeric attributes; \(d^o,\) number of nominal attributes; c, number of classes
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