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If we use Eq. ( 2.157 ) to compute the association coefficients of the IFSs y i
(
=
,
,...,
)
= (
c ij ) 6 × 6 , where c ij
=
c 1 (
y i ,
y j )
i
1
2
9
, then the association matrix C
,
i
,
j
=
1
,
2
,...,
9 will be:
1
.
000 0
.
971 0
.
931 0
.
960 0
.
945 0
.
933 0
.
934 0
.
943 0
.
948
0
.
971 1
.
000 0
.
973 0
.
956 0
.
970 0
.
970 0
.
971 0
.
972 0
.
970
0
.
931 0
.
973 1
.
000 0
.
945 0
.
968 0
.
964 0
.
965 0
.
973 0
.
953
0
.
960 0
.
956 0
.
945 1
.
000 0
.
962 0
.
923 0
.
952 0
.
950 0
.
938
C
=
0
.
945 0
.
970 0
.
968 0
.
962 1
.
000 0
.
967 0
.
946 0
.
965 0
.
985
0
.
933 0
.
970 0
.
964 0
.
923 0
.
967 1
.
000 0
.
963 0
.
969 0
.
971
0
.
934 0
.
971 0
.
965 0
.
952 0
.
946 0
.
963 1
.
000 0
.
960 0
.
923
.
.
.
.
.
.
.
.
.
0
943 0
972 0
973 0
950 0
965 0
969 0
960 1
000 0
960
.
.
.
.
.
.
.
.
.
0
948 0
970 0
953 0
938 0
985 0
971 0
923 0
960 1
000
If we use Eq. ( 2.159 ) to compute the association coefficients of the IFSs y i
(
i
=
1
,
2
,...,
9
)
, then the association matrix C
= (
c ij ) m × m , where c ij =
c 3 (
y i ,
y j )
,
i
,
j
=
1
,
2
,...,
9 will be:
1
.
000 0
.
964 0
.
917 0
.
952 0
.
947 0
.
914 0
.
914 0
.
934 0
.
933
0
.
964 1
.
000 0
.
948 0
.
941 0
.
963 0
.
959 0
.
950 0
.
959 0
.
964
0
.
917 0
.
948 1
.
000 0
.
946 0
.
957 0
.
945 0
.
948 0
.
969 0
.
936
0
.
952 0
.
941 0
.
946 1
.
000 0
.
957 0
.
908 0
.
934 0
.
950 0
.
923
C
=
0
.
947 0
.
963 0
.
957 0
.
957 1
.
000 0
.
950 0
.
930 0
.
960 0
.
976
0
.
914 0
.
959 0
.
945 0
.
908 0
.
950 1
.
000 0
.
956 0
.
953 0
.
961
0
.
914 0
.
950 0
.
948 0
.
934 0
.
930 0
.
956 1
.
000 0
.
947 0
.
911
0
.
934 0
.
959 0
.
969 0
.
950 0
.
960 0
.
953 0
.
947 1
.
000 0
.
955
0
.
933 0
.
964 0
.
936 0
.
923 0
.
976 0
.
961 0
.
911 0
.
955 1
.
000
Based on the above two association matrices, using the intuitionistic fuzzy Boole
clustering method, we can make comparisons between the clustering results of the
two association coefficients (See Table 2.15 ) (Zhao et al. 2012b).
We can see from Table 2.15 that Eq. ( 2.159 ) can derive more detailed clustering
results than Eq. ( 2.157 ). Since Eq. ( 2.157 ) cannot guarantee the necessity in the con-
dition (2) of Definition 2.31, and omits the hesitation degree, some information may
be missing. Namely, Eq. ( 2.157 ) cannot reflect all the information that the intuition-
istic fuzzy data contains. Considering the stated reasons above, it is not hard for us
to comprehend why Eq. ( 2.159 ) can get more detailed types than Eq. ( 2.157 ). There-
fore, Compared to Eq. ( 2.157 ), Eq. ( 2.159 ) has much more potential for practical
applications.
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