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1
6
(
=
1
−
0
.
2
+
0
.
1
+
0
.
2
+
0
.
0
+
0
.
0
+
0
.
1
)
1
6
(
−
0
.
1
+
0
.
0
+
0
.
0
+
0
.
1
+
0
.
2
+
0
.
2
)
=
0
.
8
1
6
(
˙
v
12
=
0
.
2
+
0
.
1
+
0
.
2
+
0
.
0
+
0
.
0
+
0
.
1
)
=
0
.
1
and then calculate the others in a similar way. Consequently, we get the intuitionistic
fuzzy similarity matrix:
⎛
⎞
(
1
,
0
)
(
0
.
8
,
0
.
1
)(
0
.
72
,
0
.
12
)(
0
.
75
,
0
.
13
)(
0
.
65
,
0
.
22
)
⎝
⎠
(
0
.
8
,
0
.
1
)
(
1
,
0
)
(
0
.
82
,
0
.
08
)(
0
.
72
,
0
.
1
)(
0
.
68
,
0
.
18
)
Z
=
(
0
.
72
,
0
.
12
)(
0
.
82
,
0
.
08
)
(
1
,
0
)
(
0
.
7
,
0
.
05
)(
0
.
63
,
0
.
23
)
(
0
.
75
,
0
.
13
)(
0
.
72
,
0
.
1
)(
0
.
7
,
0
.
05
)
(
1
,
0
)
(
0
.
63
,
0
.
25
)
(
0
.
65
,
0
.
22
)(
0
.
68
,
0
.
18
)(
0
.
63
,
0
.
23
)(
0
.
63
,
0
.
25
)
(
1
,
0
)
Step 2
Delete all the elements above the diagonal and replace the elements on the
diagonal in
Z
with the symbol of the alternatives
y
i
(
i
=
1
,
2
,
3
,
4
,
5
)
:
⎛
⎝
⎞
⎠
y
1
(
0
.
8
,
0
.
1
)
y
2
Z
=
(
0
.
72
,
0
.
12
)(
0
.
82
,
0
.
08
)
y
3
(
0
.
75
,
0
.
13
)(
0
.
72
,
0
.
1
)(
0
.
7
,
0
.
05
)
y
4
(
0
.
65
,
0
.
22
)(
0
.
68
,
0
.
18
)(
0
.
63
,
0
.
23
)(
0
.
63
,
0
.
25
)
y
5
properly, and get the corresponding clus-
tering results with intuitionistic fuzzy netting method:
Step 3
Choose the confidence level
λ
(1) When 0
.
82
<λ
≤
1
.
0, we have
⎛
⎝
⎞
⎠
y
1
y
2
Z
=
y
3
y
4
y
5
and then each car is clustered into a type:
{
y
1
}
,
{
y
2
}
,
{
y
3
}
,
{
y
4
}
,
{
y
5
}
.
(2) When 0
.
8
<λ
≤
0
.
82, we have
⎛
y
⎞
1
⎜
⎟
y
⎜
⎟
2
⎜
⎟
=
⎜
*
Z
y
3
⎟
y
⎜
⎟
4
⎜
⎟
y
⎝
⎠
5
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