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se
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e
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re
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t
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rk
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th
a
t
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se
t
h
e
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ter
n
e
t.
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h
i
s
re
se
a
rc
h
a
ims
to
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e
term
in
e
th
e
n
u
m
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e
r
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f
p
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rt
-
ti
m
e
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rk
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rs t
h
a
t
u
se
th
e
i
n
t
e
rn
e
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sin
g
t
h
e
k
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a
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ro
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g
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ti
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AP)
c
lu
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h
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m
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th
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n
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AP
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ra
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e
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rs
1
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2
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d
3
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v
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3
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n
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m
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o
n
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Jo
m
b
a
n
g
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a
n
d
S
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b
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d
istri
c
ts.
K
ey
w
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r
d
s
:
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lu
s
ter
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n
ter
n
et
K
-
af
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ity
p
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r
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t
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tim
e
wo
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k
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s
T
h
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s
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p
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n
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c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Mu
h
am
m
ad
M
u
h
ajir
Dep
ar
tm
en
t o
f
Statis
tics
,
Facu
lty
o
f
Ma
th
em
atics a
n
d
Natu
r
a
l Scie
n
ce
s
Un
iv
er
s
itas
I
s
lam
I
n
d
o
n
esia
Pro
f
.
Dr
.
H.
Z
a
n
za
wi
So
ejo
eti
B
u
ild
in
g
,
UI
I
I
n
teg
r
ated
C
am
p
u
s
,
Yo
g
y
a
k
ar
ta,
I
n
d
o
n
esia
E
-
m
ail: m
m
u
h
ajir
@
u
ii.a
c.
id
1.
I
NT
RO
D
UCT
I
O
N
L
ab
o
r
is
th
e
p
r
o
ce
s
s
b
y
wh
ic
h
a
p
er
s
o
n
r
en
d
er
s
s
er
v
ices
to
th
e
p
u
b
lic
i
n
o
r
d
er
to
ea
r
n
a
liv
in
g
.
An
in
d
iv
id
u
al
th
at
ca
r
r
ies
o
u
t
ce
r
tain
task
s
an
d
,
in
r
etu
r
n
r
ec
ei
v
es
wag
es,
an
d
o
th
er
f
o
r
m
s
o
f
co
m
p
en
s
atio
n
ar
e
ca
lled
a
wo
r
k
er
[
1
]
.
B
ased
o
n
th
e
m
an
p
o
wer
co
n
ce
p
t
,
t
h
e
wo
r
k
f
o
r
ce
is
d
iv
id
ed
in
to
two
,
n
am
ely
lab
o
r
a
n
d
non
-
lab
o
r
f
o
r
ce
s
.
T
h
e
wo
r
k
f
o
r
ce
co
m
p
r
is
es
f
u
ll
-
tim
e
an
d
n
o
n
-
f
u
ll
-
tim
e
wo
r
k
e
r
s
ag
ed
1
5
y
ea
r
s
an
d
ab
o
v
e,
th
at
do
n
o
t
h
av
e
a
p
e
r
m
an
en
t
s
o
u
r
ce
o
f
in
co
m
e
.
Me
an
wh
ile,
s
tu
d
en
ts
an
d
p
eo
p
le
r
esp
o
n
s
ib
le
f
o
r
m
an
a
g
in
g
h
o
u
s
eh
o
ld
a
f
f
air
s
an
d
o
th
er
non
-
p
er
s
o
n
al
ac
tiv
ities
ar
e
ex
clu
d
ed
.
Fu
ll
-
tim
e
wo
r
k
er
s
wo
r
k
f
o
r
m
o
r
e
t
h
an
3
5
h
o
u
r
s
a
wee
k
wh
ile
non
-
f
u
l
l
-
tim
e
wo
r
k
f
o
r
a
less
er
n
u
m
b
er
o
f
h
o
u
r
s
.
No
n
-
f
u
ll
-
tim
e
wo
r
k
er
s
ar
e
class
if
ied
in
to
2
ca
teg
o
r
ies
,
n
am
ely
u
n
d
e
r
em
p
lo
y
ed
/s
em
i
-
u
n
em
p
lo
y
ed
(
f
r
ee
lan
ce
)
an
d
p
ar
t
-
tim
e
wo
r
k
er
s
.
S
em
i
-
u
n
em
p
l
o
y
ed
wo
r
k
er
s
(
f
r
ee
lan
ce
)
ar
e
th
o
s
e
th
at
wo
r
k
f
o
r
less
th
an
3
5
h
o
u
r
s
a
wee
k
an
d
ar
e
willin
g
to
ac
ce
p
t
o
th
er
jo
b
o
f
f
e
r
s
.
C
o
n
v
er
s
ely
,
p
ar
t
-
tim
e
wo
r
k
er
s
ar
e
th
o
s
e
th
at
ar
e
n
o
t
s
ea
r
ch
in
g
o
r
willin
g
to
ac
ce
p
t
an
o
th
er
jo
b
[
2
]
.
T
h
e
p
o
p
u
latio
n
en
ter
i
n
g
th
e
lab
o
r
f
o
r
ce
in
I
n
d
o
n
esia
is
s
h
o
wn
in
T
ab
le
1
[
3
]
.
Acc
o
r
d
in
g
to
T
ab
le
1
,
av
er
ag
e
in
cr
ea
s
es
o
f
ap
p
r
o
x
im
ately
2
.
0
7
%
o
f
th
e
I
n
d
o
n
esian
p
o
p
u
latio
n
en
ter
th
e
lab
o
r
f
o
r
ce
y
ea
r
ly
.
An
im
p
o
r
tan
t
co
m
p
o
n
en
t
o
f
t
h
is
ar
r
an
g
em
e
n
t
is
p
ar
t
-
tim
e
wo
r
k
wh
ich
is
o
n
e
o
f
th
e
th
r
ea
ts
to
ec
o
n
o
m
ic
an
d
s
o
cial
ch
an
g
e.
T
h
is
is
r
elate
d
to
th
e
in
cr
ea
s
in
g
d
iv
e
r
s
ity
o
f
th
e
wo
r
k
f
o
r
ce
an
d
its
ass
o
ciate
d
ch
an
g
es.
Ma
n
y
d
is
p
u
tes
ass
o
ciate
d
with
g
r
o
wth
ar
e
b
ased
o
n
p
a
r
t
-
tim
e
e
m
p
lo
y
m
e
n
t,
wh
ich
p
o
s
itiv
ely
im
p
ac
ts
s
o
ciety
as
a
wh
o
le.
Un
til
n
o
w,
th
e
s
tig
m
a
ass
o
ciate
d
with
p
a
r
t
-
tim
e
w
o
r
k
er
s
is
c
o
n
s
id
er
ed
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
K
-
a
ffin
ity
p
r
o
p
a
g
a
tio
n
clu
s
teri
n
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a
lg
o
r
ith
m
fo
r
th
e
cla
s
s
ifica
tio
n
o
f p
a
r
t
-
time…
(
N
o
ve
n
d
r
i I
s
r
a
A
s
r
in
y
)
465
unf
av
o
r
ab
le
b
ec
a
u
s
e
th
e
wag
e
s
r
ec
eiv
ed
ar
e
r
elativ
ely
s
m
all.
Ho
wev
er
,
it
allo
ws
in
d
iv
id
u
al
s
to
co
m
b
in
e
wo
r
k
with
o
th
er
ac
tiv
ities
s
u
ch
as e
-
co
m
m
er
ce
-
b
ased
b
u
s
in
ess
es,
s
tu
d
y
in
g
,
o
r
r
aisi
n
g
a
f
am
ily
[
4
]
.
T
ab
le
1
.
Po
p
u
latio
n
en
te
r
in
g
t
h
e
lab
o
r
f
o
r
ce
i
n
I
n
d
o
n
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Y
e
a
r
Th
e
n
u
m
b
e
r
o
f
w
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r
k
e
r
s
2
0
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6
1
2
5
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4
4
1
,
748
2
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1
7
1
2
8
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2
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746
2
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641
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6
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,
880
Acc
o
r
d
in
g
t
o
Ma
r
tin
an
d
L
al
e
[
5
]
,
[
6
]
,
p
ar
t
-
tim
e
wo
r
k
is
in
cr
ea
s
in
g
ly
r
elev
a
n
t
in
m
a
n
y
d
ev
elo
p
e
d
co
u
n
tr
ies
b
ec
au
s
e
it
allo
ws
w
o
r
k
er
s
to
p
lay
a
d
u
al
r
o
le
in
th
e
m
o
d
er
n
lab
o
r
m
ar
k
et.
I
n
"n
o
r
m
al
tim
e,
"
ce
r
tain
g
r
o
u
p
s
o
f
em
p
l
o
y
er
s
o
f
f
er
alte
r
n
ativ
e
wo
r
k
a
r
r
an
g
em
en
ts
,
in
clu
d
in
g
f
u
ll
-
tim
e
em
p
lo
y
m
en
t
.
P
ar
t
-
tim
e
jo
b
s
ar
e
u
s
u
ally
o
n
th
e
i
n
cr
ea
s
e
d
u
r
in
g
a
r
ec
ess
io
n
b
ec
a
u
s
e
it
p
r
o
m
p
ts
wo
r
k
er
s
an
d
co
m
p
an
i
es
to
m
ak
e
ce
r
tain
ad
ju
s
tm
en
ts
to
th
e
n
ew
ec
o
n
o
m
ic
co
n
d
itio
n
s
.
T
h
is
tak
es
s
ev
er
al
f
o
r
m
s
an
d
d
if
f
er
s
am
o
n
g
em
p
lo
y
ee
s
an
d
estab
lis
h
m
en
ts
.
B
esid
es,
p
ar
t
-
tim
e
wo
r
k
er
s
r
esp
o
n
d
ed
to
t
h
ese
ad
ju
s
tm
en
ts
,
esp
ec
ially
d
u
r
in
g
an
ec
o
n
o
m
ic
d
o
wn
tu
r
n
(
r
ec
ess
io
n
)
.
T
h
e
c
y
clica
l
in
cr
ea
s
e
is
lar
g
ely
u
n
in
ten
tio
n
al
an
d
is
wid
esp
r
e
ad
ac
r
o
s
s
d
if
f
er
en
t
s
eg
m
en
ts
o
f
th
e
lab
o
r
m
ar
k
et.
Ov
er
all,
b
ased
o
n
th
e
av
ailab
le
ev
id
en
ce
,
th
e
f
lex
ib
ilit
y
af
f
o
r
d
ed
b
y
p
ar
t
-
tim
e
wo
r
k
a
r
r
an
g
e
m
en
ts
ap
p
ea
r
s
t
o
b
e
b
r
o
ad
ly
p
o
s
itiv
e.
T
h
e
tr
en
d
o
f
I
n
d
o
n
esian
p
ar
t
-
tim
e
wo
r
k
er
s
is
s
h
o
wn
in
T
ab
le
2
[
3
]
.
T
ab
le
2
.
T
r
e
n
d
s
o
f
p
ar
t
-
tim
e
w
o
r
k
er
s
in
I
n
d
o
n
esia
Y
e
a
r
Th
e
n
u
m
b
e
r
o
f
w
o
r
k
e
r
s
2
0
1
6
23
,
257
,
8
8
7
2
0
1
7
24
,
674
,
7
3
7
2
0
1
8
27
,
371
,
5
1
7
2
0
1
9
28
,
405
,
7
8
7
B
as
e
d
o
n
T
a
b
l
e
2
,
t
h
e
a
v
e
r
a
g
e
i
n
c
r
e
a
s
e
i
n
t
h
e
p
o
p
u
l
a
t
i
o
n
o
f
p
a
r
t
-
t
i
m
e
w
o
r
k
e
r
s
i
n
I
n
d
o
n
e
s
ia
i
s
6
.
4
%
.
H
o
w
e
v
e
r
,
t
h
is
r
e
f
l
e
c
ts
t
h
e
s
u
p
p
l
y
a
n
d
d
e
m
a
n
d
f
a
c
t
o
r
s
i
n
th
e
l
a
b
o
r
m
a
r
k
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t
.
T
h
e
m
a
r
k
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t
r
e
s
e
a
r
c
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M
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r
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I
n
d
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i
a
r
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d
6
t
h
g
l
o
b
a
l
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y
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w
i
t
h
8
3
.
7
m
i
ll
i
o
n
p
e
o
p
l
e
i
n
2
0
1
4
[
7
]
.
A
c
c
o
r
d
i
n
g
to
A
PJ
I
I
&
P
o
l
li
n
g
[
8
]
,
i
n
2
0
1
8
,
t
h
e
t
o
t
a
l
p
o
p
u
l
a
t
i
o
n
wa
s
2
6
4
.
1
6
m
i
l
l
i
o
n
p
e
o
p
l
e
,
o
u
t
o
f
w
h
i
c
h
6
4
.
8
%
(
1
7
1
.
1
7
m
i
l
l
i
o
n
)
w
e
r
e
i
n
t
e
r
n
e
t
u
s
e
r
s
.
I
n
2
0
1
7
,
t
h
e
n
u
m
b
e
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f
i
n
t
e
r
n
et
u
s
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r
s
a
n
d
t
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t
o
t
al
p
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1
4
3
.
2
6
m
i
l
l
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o
n
a
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d
2
6
2
m
i
l
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i
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n
.
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h
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c
o
n
t
r
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b
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t
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5
5
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a
n
d
1
3
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5
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o
f
p
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m
J
a
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t
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n
d
E
as
t
J
av
a
P
r
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v
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e
.
I
n
2
0
1
9
,
th
e
SAKE
R
NAS
d
at
a
ac
q
u
ir
ed
f
o
r
p
er
io
d
2
s
h
o
we
d
th
at
th
e
5
m
o
s
t
d
o
m
in
a
n
t
p
r
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v
in
ce
s
in
ter
m
s
o
f
p
ar
t
-
tim
e
wo
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k
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s
th
at
d
is
ch
ar
g
e
th
eir
d
u
ties
u
s
in
g
th
e
in
ter
n
et
ar
e
E
ast,
C
en
tr
al
an
d
W
est
J
av
a
a
s
well
as
Ace
h
an
d
No
r
th
Su
m
atr
a
with
2
0
3
8
,
1
6
6
1
,
1
2
3
1
,
8
6
0
,
an
d
7
6
4
wo
r
k
er
s
r
esp
ec
tiv
ely
[
3
]
.
B
ased
o
n
th
is
an
aly
s
is
,
th
e
p
r
o
v
i
n
ce
o
f
E
ast
J
av
a
h
as
th
e
h
ig
h
est
n
u
m
b
er
o
f
wo
r
k
e
r
s
,
h
en
ce
it
is
u
s
ed
a
s
th
e
o
b
ject
o
f
th
is
r
esear
ch
.
T
h
e
r
ef
o
r
e,
to
in
cr
ea
s
e
th
e
av
ailab
ilit
y
o
f
th
e
p
ar
t
-
tim
e
lab
o
r
m
ar
k
et
an
d
th
e
wo
r
k
er
s
’
wel
f
ar
e,
ea
ch
d
is
tr
ict/city
in
E
ast J
av
a
wa
s
g
r
o
u
p
e
d
in
ac
co
r
d
an
ce
with
r
el
ated
v
ar
iab
les.
T
h
ese
in
clu
d
e
u
s
in
g
th
e
in
ter
n
et
as
a
m
ea
n
s
o
f
c
o
m
m
u
n
icatio
n
at
wo
r
k
,
p
r
o
m
o
tio
n
a
ctiv
ities
,
a
n
d
th
e
s
ellin
g
o
f
g
o
o
d
s
an
d
s
er
v
ices
th
r
o
u
g
h
e
-
m
ails
,
s
o
cial
m
ed
ia,
web
s
ite
s
,
an
d
m
ar
k
etp
lace
ap
p
licatio
n
s
[
9
]
.
T
h
is
aid
s
th
e
g
o
v
er
n
m
e
n
t,
esp
ec
ially
th
e
E
ast
J
av
a
p
r
o
v
in
cial
m
an
p
o
wer
o
f
f
ice,
in
en
ac
tin
g
r
eg
u
latio
n
s
r
ela
ted
to
th
e
em
p
lo
y
m
e
n
t o
f
p
ar
t
-
tim
e
wo
r
k
er
s
.
T
h
e
d
is
tr
icts
an
d
cities
in
E
as
t
J
av
a
wer
e
g
r
o
u
p
e
d
u
s
in
g
th
e
clu
s
ter
in
g
af
f
i
n
ity
p
r
o
p
ag
atio
n
(K
-
AP)
.
T
h
is
m
eth
o
d
was a
d
o
p
ted
t
o
o
b
tain
th
e
o
p
tim
al
n
u
m
b
er
o
f
e
x
em
p
lar
s
an
d
o
b
jects th
r
o
u
g
h
af
f
in
ity
p
r
o
p
ag
atio
n
(
AP)
.
On
e
ad
v
an
tag
e
o
f
th
is
ap
p
r
o
ac
h
is
th
at
th
e
n
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m
b
er
o
f
k
n
ee
d
n
o
t
b
e
en
te
r
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b
eg
in
n
in
g
,
b
esid
es
r
elativ
ely
s
m
all
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r
o
r
s
ten
d
t
o
o
cc
u
r
wh
e
n
lar
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atasets
ar
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u
s
ed
co
m
p
ar
e
d
to
o
th
er
clu
s
ter
m
eth
o
d
s
[
1
0
]
.
T
h
e
K
-
Af
f
in
ity
Pro
p
ag
atio
n
m
et
h
o
d
is
m
o
r
e
s
tab
le
th
an
th
e
K
-
M
ea
n
s
,
wh
er
e
th
e
o
p
tim
al
clu
s
te
r
is
o
b
tain
ed
u
s
in
g
C
-
I
n
d
ex
,
Dav
ies B
o
u
ld
in
,
an
d
C
o
n
n
ec
tiv
ity
[
1
1
]
,
[
1
2
]
.
B
ased
o
n
th
e
a
f
o
r
em
e
n
tio
n
ed
d
escr
ip
tio
n
,
th
is
re
s
ea
r
ch
aim
s
to
d
eter
m
in
e
th
e
g
r
o
u
p
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n
g
o
f
p
ar
t
-
tim
e
wo
r
k
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s
in
E
ast
J
av
a
p
r
o
v
in
ce
u
s
in
g
th
e
"K
-
Af
f
in
ity
Pro
p
a
g
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n
"
m
eth
o
d
,
also
k
n
o
wn
as
K
-
AP
C
lu
s
ter
in
g
.
T
h
is
en
ab
led
p
o
licy
m
a
k
er
t
o
c
o
n
s
id
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th
e
ad
o
p
tio
n
o
f
ce
r
tain
s
tep
s
o
r
d
ec
is
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n
s
r
elate
d
t
o
th
e
im
p
lem
en
tatio
n
o
f
f
u
tu
r
e
in
n
o
v
atio
n
s
in
th
e
la
b
o
r
s
ec
to
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
1
,
Octo
b
er
2
0
2
1
:
464
-
4
7
2
466
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.1
.
Clus
t
er
ing
m
et
ho
d
T
h
e
clu
s
ter
in
g
m
eth
o
d
is
a
m
ea
n
s
o
f
g
r
o
u
p
i
n
g
o
b
jects
with
s
im
ilar
v
alu
es
an
d
ch
a
r
ac
ter
i
s
tics
.
I
t
is
co
m
m
o
n
l
y
u
s
ed
to
g
ain
in
s
ig
h
t,
s
tatis
tica
l
an
d
im
ag
e
an
aly
s
e
s
,
m
ac
h
in
e
lear
n
in
g
,
d
ata
p
atter
n
s
,
an
d
r
etr
iev
e
in
f
o
r
m
atio
n
[
1
3
]
.
I
n
g
en
e
r
al,
th
e
g
r
o
u
p
i
n
g
in
v
o
lv
es
g
r
o
u
p
s
with
s
m
all
d
is
tan
ce
s
b
etwe
e
n
o
b
jects,
th
e
ar
ea
d
en
s
ity
o
f
th
e
d
ata
s
p
ac
e,
a
n
d
th
e
in
ter
v
al
o
r
d
is
tr
ib
u
tio
n
o
f
ce
r
tain
s
tatis
tic
s
.
T
h
er
ef
o
r
e
,
th
is
m
eth
o
d
s
er
v
es
as
a
r
ef
er
en
ce
f
o
r
t
h
e
o
p
tim
izatio
n
o
f
m
u
lti
-
p
u
r
p
o
s
e
p
r
o
b
lem
s
[
1
4
]
.
Gr
o
u
p
in
g
is
ca
r
r
ied
o
u
t
b
ase
d
o
n
th
e
d
is
tan
ce
b
etwe
en
o
b
jects.
T
h
e
s
h
a
p
e
o
f
t
h
e
clu
s
ter
is
o
n
l
y
af
f
ec
ted
b
y
th
e
s
ize
o
f
th
ese
d
is
tan
ce
s
.
Acc
o
r
d
in
g
to
Ma
h
es
war
i
[
1
5
]
a
n
d
C
h
ar
r
a
d
et
a
l
.
[
1
6
]
,
th
is
p
r
o
ce
s
s
is
ca
lcu
lated
u
s
in
g
th
e
E
u
clid
ea
n
d
is
tan
ce
as f
o
llo
ws.
Dis
tan
ce
with
E
u
clid
ea
n
(
,
)
=
√
∑
(
−
)
2
=
1
(
1
)
wh
er
e
(
,
)
=
eu
clid
iean
d
is
tan
ce
b
etwe
en
o
b
ject,
,
=
o
b
ject
co
o
r
d
in
ate,
=
n
u
m
b
e
r
o
f
v
ar
iab
les
2
.
2
.
Wit
hin
s
um
-
of
-
s
qu
a
re
(
WSS
)
m
et
ho
d
W
SS
is
o
n
e
o
f
th
e
m
eth
o
d
s
u
s
ed
to
ev
alu
ate
in
tr
ac
lu
s
ter
v
ar
i
ab
ilit
y
.
G
en
er
ally
,
a
clu
s
ter
with
a
s
m
all
W
SS
is
m
o
r
e
co
m
p
ac
t
th
an
o
n
e
with
a
lar
g
e
s
q
u
ar
e
s
u
m
.
E
ac
h
o
b
s
er
v
atio
n
is
allo
ca
ted
t
o
th
e
n
ea
r
est
clu
s
ter
with
th
e
d
is
tan
ce
ca
lcu
lated
u
s
in
g
th
e
C
o
s
in
e
Similar
i
ty
b
etwe
en
th
e
o
b
s
er
v
atio
n
an
d
clu
s
ter
m
id
p
o
in
t
(
ce
n
tr
o
id
)
.
Fu
r
th
er
m
o
r
e
,
ea
ch
ce
n
tr
o
id
is
an
av
er
ag
e
of
th
e
o
b
s
er
v
atio
n
s
in
ea
ch
clu
s
ter
.
T
h
e
f
o
r
m
u
la
f
o
r
W
SS
is
s
tated
as f
o
llo
ws
[
1
7
]
,
[
1
8
]
:
∑
∑
∑
(
−
)
2
=
1
∈
=
1
(
2
)
i
n
f
o
r
m
atio
n
=
Sam
p
le
f
r
o
m
th
e
s
et
-
in
clu
s
ter
k
=
T
h
e
v
ar
ia
b
le
f
r
o
m
clu
s
ter
j t
o
th
at
o
f
k
2
.
3
.
K
-
a
f
f
ini
t
y
p
ro
pa
g
a
t
io
n
K
-
af
f
in
ity
p
r
o
p
a
g
atio
n
(K
-
AP
)
m
o
d
if
icatio
n
o
f
th
e
AP
m
et
h
o
d
aim
s
to
p
r
o
d
u
ce
an
o
p
tim
al
n
u
m
b
er
o
f
ex
em
p
lar
s
.
T
h
is
n
ew
m
eth
o
d
id
en
tifie
s
ex
em
p
lar
s
,
th
er
e
b
y
f
o
r
m
in
g
d
ata
p
o
in
t
clu
s
ter
s
.
K
is
co
m
p
ar
ed
b
y
s
ev
er
al
in
d
ices
to
d
eter
m
in
e
its
o
p
tim
al
f
o
r
m
J
ia
et
a
l.
[
1
9
]
.
K
-
AP
p
r
o
d
u
ce
s
K
clu
s
ter
s
b
ased
o
n
p
r
ed
eter
m
in
e
d
n
ee
d
s
an
d
p
ar
am
eter
s
in
ter
m
s
o
f
d
eter
m
i
n
in
g
r
u
les
o
r
c
o
n
tr
o
ls
in
th
e
m
ess
ag
e
d
eliv
er
y
p
r
o
ce
s
s
.
An
o
th
er
ad
v
a
n
tag
e
o
f
th
is
m
eth
o
d
is
th
e
b
elief
in
an
o
b
ject
to
s
er
v
e
as
an
ex
em
p
lar
wh
ich
is
au
to
m
atica
lly
ad
ap
ted
b
y
K
-
AP,
wh
ile
th
e
AP
is
a
p
ar
am
eter
s
et
b
y
its
u
s
er
s
[
2
0
]
.
B
e
s
id
es,
th
e
o
v
er
h
ea
d
(
m
em
o
r
y
u
s
ag
e
d
u
r
in
g
p
r
o
ce
s
s
in
g
)
co
m
p
u
tatio
n
o
f
K
-
AP
is
in
s
ig
n
if
ican
t
co
m
p
ar
e
d
to
th
e
AP.
T
h
e
alg
o
r
ith
m
o
f
K
-
AP
id
en
tifie
s
ex
em
p
lar
s
b
y
r
ec
u
r
s
iv
ely
s
en
d
in
g
r
ea
l
-
v
a
lu
ed
m
ess
ag
es
b
etwe
en
p
air
s
o
f
d
ata
p
o
i
n
ts
,
th
is
was in
s
p
ir
ed
b
y
AP
[
2
1
]
.
T
h
e
n
u
m
b
er
o
f
id
en
tifie
d
ex
em
p
lar
s
(
clu
s
ter
s
)
i
s
in
f
lu
en
ce
d
b
y
th
e
in
p
u
t p
r
ef
er
en
c
es
v
alu
es,
alth
o
u
g
h
it
also
em
er
g
es
f
r
o
m
th
e
m
ess
ag
e
-
p
ass
in
g
p
r
o
ce
d
u
r
e.
T
h
e
s
u
m
o
f
r
(
i,
k)
an
d
a
(
i,
k)
is
u
s
ed
to
d
eter
m
in
e
wh
eth
er
o
r
n
o
t
th
e
co
r
r
esp
o
n
d
in
g
d
ata
p
o
in
t
is
a
ca
n
d
id
ate
ex
em
p
lar
k
[
2
2
]
,
[
2
3
]
.
Af
ter
a
d
ata
p
o
in
t
h
as
b
ee
n
s
elec
ted
,
th
o
s
e
p
lace
d
clo
s
er
to
co
m
p
etin
g
ca
n
d
id
a
te
ex
em
p
lar
′
ar
e
ass
ig
n
ed
to
th
is
clu
s
ter
.
K
-
A
P
g
en
er
ates
k
clu
s
ter
s
b
y
ad
d
in
g
co
n
s
tr
ain
ts
in
th
e
p
r
o
ce
s
s
o
f
s
wap
p
in
g
m
ess
ag
es
to
lim
it
its
n
u
m
b
er
wh
ile
m
ain
tain
in
g
all
AP c
lu
s
ter
in
g
ad
v
an
tag
es
[
2
4
]
,
[
2
5
]
.
T
h
e
alg
o
r
ith
m
o
f
K
-
AP is st
ated
as f
o
llo
ws
[
2
6
]
.
1.
I
n
p
u
t similar
ities
m
atr
ix
(
,
)
{
(
,
)
,
∈
{
1
,
…
,
}
≠
,
}
(
4
)
2.
I
n
itialize
th
e
av
ailab
ilit
y
(
,
)
,
an
d
co
n
f
id
en
ce
m
atr
ix
(
)
(
,
)
=
0
(
5
)
(
)
=
min
(
)
(
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
K
-
a
ffin
ity
p
r
o
p
a
g
a
tio
n
clu
s
teri
n
g
a
lg
o
r
ith
m
fo
r
th
e
cla
s
s
ifica
tio
n
o
f p
a
r
t
-
time…
(
N
o
ve
n
d
r
i I
s
r
a
A
s
r
in
y
)
467
3.
R
en
ew
r
esp
o
n
s
ib
ilit
ies
(
,
)
(
,
)
=
(
,
)
−
ma
x
{
(
)
+
(
,
)
}
,
ma
x
′
:
′
∈
{
,
}
{
(
,
′
)
+
(
,
′
)
}
(
7
)
4.
R
en
ewin
g
s
elf
-
r
esp
o
n
s
ib
ilit
y
(
,
)
(
,
)
=
(
)
−
ma
x
′
:
′
≠
{
(
,
′
)
+
(
,
′
)
}
(
8
)
5.
Up
d
atin
g
th
e
a
v
ailab
ilit
ies m
atr
ix
(
,
)
←
min
{
0
,
(
,
)
+
∉
{
,
}
{
0
,
(
′
,
)
}
}
(
9
)
6.
R
en
ewin
g
s
elf
-
av
ailab
ilit
y
(
,
)
(
,
)
=
Σ
′
∉
{
,
}
ma
x
{
0
,
(
′
,
)
}
(
1
0
)
7.
R
en
ew
co
n
f
id
en
ce
(
)
(
)
=
(
,
)
−
ma
x
′
:
′
≠
{
(
,
′
)
+
(
,
′
)
}
(
1
1
)
(
)
=
−
(
{
(
)
,
≠
}
)
(
1
2
)
8.
C
o
m
b
in
atio
n
o
f
av
ailab
ilit
y
an
d
r
esp
o
n
s
ib
ilit
y
(
,
)
[
27
]
(
,
)
=
{
(
,
)
+
(
,
)
}
(
1
3
)
2.
4
.
St
a
nd
a
rd
d
ev
ia
t
io
n
T
h
e
g
o
o
d
n
ess
o
f
a
clu
s
ter
was
d
eter
m
in
e
d
u
s
in
g
t
h
e
s
tan
d
ar
d
d
e
v
iatio
n
v
alu
e
[
2
8
]
,
[
2
9
]
.
T
h
e
(
1
4
)
is
th
e
in
tr
a
-
clu
s
ter
s
tan
d
ar
d
d
ev
i
atio
n
e
q
u
atio
n
,
=
−
1
∑
=
1
(
1
4
)
wh
er
e
S
k
is
th
e
s
tan
d
ar
d
d
e
v
ia
tio
n
f
o
r
a
v
ar
iab
le
k
f
r
o
m
clu
s
t
er
K.
T
h
e
e
q
u
atio
n
o
f
th
e
i
n
ter
-
clu
s
ter
s
tan
d
ar
d
d
ev
iatio
n
is
s
tated
as
(
1
5
)
,
=
[
(
−
1
)
−
1
∑
(
̅
−
̅
)
2
=
1
]
1
2
(
1
5
)
wh
er
e
̅
is
th
e
clu
s
ter
av
er
ag
e
f
o
r
a
p
a
r
ticu
lar
v
a
r
iab
le
an
d
̅
is
th
e
to
tal
av
er
a
g
e
f
o
r
all
K
clu
s
ter
s
.
T
h
e
b
es
t
m
eth
o
d
is
th
e
o
n
e
with
th
e
s
m
allest
r
atio
v
alu
e,
af
ter
d
iv
id
in
g
Sw
by
Sb
.
Ho
wev
er
,
ass
u
m
in
g
th
e
r
e
ar
e
h
i
g
h
h
o
m
o
g
en
eity
an
d
h
eter
o
g
en
eit
y
v
alu
e
s
am
o
n
g
m
em
b
er
s
b
el
o
n
g
in
g
to
th
e
s
am
e
clu
s
ter
,
th
is
s
im
p
ly
m
ea
n
s
th
at
it
was a
p
p
r
o
p
r
iately
f
o
r
m
e
d
[
3
0
]
.
2.
5
.
M
et
ho
do
lo
g
y
S
ec
o
n
d
ar
y
d
ata
was
o
b
tai
n
ed
f
r
o
m
th
e
I
n
d
o
n
esia
Min
is
tr
y
o
f
L
ab
o
r
web
s
ite,
n
am
ely
www.
l
in
d
a.
k
em
n
ak
e
r
.
g
o
.
id
.
T
h
is
s
tu
d
y
ad
o
p
ted
t
h
e
2
0
1
9
d
ata
o
n
th
e
n
u
m
b
er
o
f
p
ar
t
-
ti
m
e
wo
r
k
e
r
s
in
E
ast
J
av
a.
T
h
e
r
esear
ch
v
ar
iab
les
in
clu
d
e
Gen
d
e
r
,
L
ev
el
o
f
E
d
u
c
atio
n
,
Hea
lth
I
n
s
u
r
a
n
ce
,
W
ag
e
Pay
m
en
t
Sy
s
tem
,
Usi
n
g
th
e
I
n
ter
n
et
at
W
o
r
k
,
T
r
ain
in
g
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Activ
ities
with
th
e
Mo
s
t
T
im
e,
T
ak
in
g
C
ar
e
o
f
Ho
u
s
eh
o
ld
,
J
o
b
Statu
s
in
Ma
in
J
o
b
,
C
o
n
tr
ac
t
Ag
r
ee
m
en
t,
Sch
o
o
l
Par
ticip
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n
,
a
n
d
Ma
r
ital
Statu
s
,
T
r
ad
e
Un
i
o
n
Me
m
b
er
,
Ag
e,
W
ag
e,
an
d
Nu
m
b
er
o
f
Ho
u
s
eh
o
ld
Me
m
b
e
r
s
.
T
h
e
K
-
Af
f
in
ity
p
r
o
p
a
g
atio
n
cl
u
s
ter
in
g
m
eth
o
d
was
ad
o
p
ted
.
T
h
e
R
Stu
d
io
s
o
f
twar
e
was
u
s
ed
to
g
r
o
u
p
d
is
tr
icts
in
E
ast
J
av
a
p
r
o
v
in
ce
b
ased
o
n
th
e
n
u
m
b
er
o
f
p
ar
t
-
tim
e
wo
r
k
e
r
s
th
at
u
s
ed
th
e
in
ter
n
et.
I
n
th
is
s
tu
d
y
,
th
e
f
ir
s
t
s
tep
is
to
d
eter
m
in
e
th
e
to
p
ic,
th
e
n
d
eter
m
in
e
th
e
p
r
o
b
lem
s
f
o
u
n
d
,
f
o
r
m
u
late
th
e
p
r
o
b
lem
,
s
ea
r
c
h
f
o
r
s
tu
d
y
liter
atu
r
e,
p
r
ep
r
o
ce
s
s
th
e
d
ata,
th
en
m
a
k
e
a
n
o
v
er
v
iew,
th
en
d
eter
m
in
e
t
h
e
n
u
m
b
er
o
f
clu
s
ter
s
with
W
SS
an
d
e
x
p
er
t
r
ec
o
m
m
en
d
atio
n
s
,
p
er
f
o
r
m
clu
s
ter
in
g
with
t
h
e
K
-
AP
alg
o
r
ith
m
.
Fin
ally
,
E
v
alu
atio
n
o
f
cl
u
s
ter
g
o
o
d
n
ess
to
d
eter
m
i
n
e
th
e
b
est
n
u
m
b
er
o
f
clu
s
ter
s
.
Fo
r
t
h
e
s
ch
em
atic
in
th
e
f
o
r
m
o
f
im
ag
es c
an
b
e
s
ee
n
in
Fig
u
r
e
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
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J
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24
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1
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1
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468
Fig
u
r
e
1
.
R
esear
ch
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tag
es u
s
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g
k
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a
f
f
in
ity
p
r
o
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g
atio
n
m
eth
o
d
3.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
I
O
N
3
.
1
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O
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er
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iew
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rt
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t
im
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a
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a
v
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B
ased
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e
tr
en
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ast
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r
s
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ar
t
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tim
e
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s
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e
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ter
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ec
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te
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r
m
ain
j
o
b
h
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e
in
cr
ea
s
ed
.
Fig
u
r
e
2
s
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o
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e
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in
ce
s
with
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e
h
i
g
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est
n
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m
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er
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f
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r
t
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e
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r
k
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s
th
at
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s
e
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e
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ter
n
et
to
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r
r
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t
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ai
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2
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1
9
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ased
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Fig
u
r
e
2
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ata.
REFEREN
CE
S
[1
]
In
d
o
n
e
sia
g
o
v
e
rn
m
e
n
t
,
In
d
o
n
e
sia
c
o
n
stit
u
ti
o
n
Nu
m
b
e
r
1
3
o
f
2
0
0
3
o
n
M
a
n
p
o
we
r,
2
0
0
3
.
[2
]
M
in
ister
o
f
M
a
n
p
o
we
r
De
c
re
e
,
In
d
o
n
e
sia
c
o
n
stit
u
io
n
Nu
m
b
e
r
2
0
6
o
f
2
0
1
7
o
n
La
b
o
r
d
e
v
e
l
o
p
m
e
n
t
i
n
d
e
x
m
e
a
su
re
m
e
n
t
g
u
id
e
li
n
e
s,
2
0
1
7
.
[3
]
S
tatisti
c
s In
d
o
n
e
sia
.
La
b
o
r
F
o
rc
e
S
it
u
a
ti
o
n
in
I
n
d
o
n
e
sia
Au
g
u
st
2
0
1
9
,
2
0
2
0
.
[4
]
P
.
S
c
h
o
u
k
e
n
s
a
n
d
A.
Ba
rri
o
,
“
Th
e
c
h
a
n
g
in
g
c
o
n
c
e
p
t
o
f
wo
r
k
:
Wh
e
n
d
o
e
s
t
y
p
ica
l
wo
rk
b
e
c
o
m
e
a
ty
p
ica
l?
,
”
Eu
ro
p
e
a
n
L
a
b
o
u
r L
a
w
J
o
u
r
n
a
l
,
v
o
l.
8
,
n
o
.
4
,
p
p
.
3
0
6
-
3
3
2
,
2
0
1
7
,
d
o
i
:
1
0
.
1
1
7
7
/2
0
3
1
9
5
2
5
1
7
7
4
3
8
7
1
.
[5
]
D.
B
.
-
M
a
rti
n
s,
“
Wh
y
d
o
e
s
p
a
rt
-
t
ime
e
m
p
lo
y
m
e
n
t
i
n
c
re
a
se
in
re
c
e
ss
io
n
s?
,
”
IZ
A
W
o
rl
d
L
a
b
o
r
,
p
p
.
397
-
3
9
7
,
2
0
1
7
,
doi
:
1
0
.
1
5
1
8
5
/
iza
wo
l.
3
9
7
.
[6
]
D.
B
.
-
M
a
rti
n
s
a
n
d
E.
Lalé
,
“
Th
e
in
s
a
n
d
o
u
ts
o
f
i
n
v
o
l
u
n
tary
p
a
rt
-
ti
m
e
e
m
p
lo
y
m
e
n
t
,”
L
a
b
o
u
r
Eco
n
.
,
v
o
l.
67
,
n
o
.
1
0
1
9
4
0
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
la
b
e
c
o
.
2
0
2
0
.
1
0
1
9
4
0
.
[7
]
D.
Dz
a
h
a
b
a
n
a
n
d
M
.
R.
S
h
i
h
a
b
,
"
Cu
sto
m
e
r
so
c
ial
e
x
p
e
rien
c
e
a
s
a
n
tec
e
d
e
n
ts
o
f
so
c
ial
c
o
m
m
e
rc
e
:
In
sig
h
ts
fro
m
Ka
sk
u
s,"
2
0
1
6
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
d
Co
m
p
u
te
r
S
c
ien
c
e
a
n
d
I
n
fo
rm
a
t
io
n
S
y
ste
ms
(ICACS
IS
)
,
2
0
1
6
,
p
p
.
2
5
1
-
2
5
6
,
d
o
i:
1
0
.
1
1
0
9
/I
CACSIS
.
2
0
1
6
.
7
8
7
2
7
6
6
.
[8
]
A.
G
u
m
e
lar,
M
.
I.
Na
su
ti
o
n
,
I.
F
.
Oe
sm
a
n
,
F
.
Ra
m
a
d
in
i,
a
n
d
M
.
I
rfa
n
,
“
Tec
h
n
o
lo
g
y
m
o
b
il
e
b
a
n
k
i
n
g
o
n
c
u
sto
m
e
r
sa
ti
sc
a
ti
o
n
”
,
J.
P
h
y
s.
C
o
n
f
.
S
e
r.
,
v
o
l.
1
5
,
n
o
.
7
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/1
4
7
7
/7
/
0
7
2
0
2
0
.
[9
]
C.
L.
Ad
k
i
n
s,
V.
F
a
rm
v
i
ll
e
,
S
.
A.
P
re
m
e
a
u
x
,
a
n
d
L.
Ro
c
k
,
“
T
h
e
u
se
o
f
c
o
m
m
u
n
ica
ti
o
n
tec
h
n
o
l
o
g
y
to
m
a
n
a
g
e
wo
rk
-
h
o
m
e
b
o
u
n
d
a
ries
,”
J
o
u
rn
a
l
o
f
Beh
a
v
io
r
a
l
a
n
d
Ap
p
li
e
d
M
a
n
a
g
e
me
n
t
,
v
o
l.
15
,
n
o
.
e
,
p
p
.
8
2
-
1
0
0
,
2
0
1
4
,
d
o
i:
10
.
2
1
8
1
8
/0
0
1
c
.
1
7
9
3
9
.
[1
0
]
X.
Zh
a
n
g
,
C.
F
u
rt
leh
n
e
r,
C
.
G
e
r
m
a
in
-
Re
n
a
u
d
a
n
d
M
.
S
e
b
a
g
,
"
Da
ta
S
trea
m
Clu
ste
rin
g
Wi
th
Affin
i
ty
P
r
o
p
a
g
a
ti
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n
,
"
in
IEE
E
T
ra
n
sa
c
ti
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n
s
o
n
K
n
o
wl
e
d
g
e
a
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d
D
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in
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,
v
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2
6
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ly
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/
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2
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3
.
1
4
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.
[1
1
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ter
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io
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2
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d
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u
sin
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lt
y
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e
rt
S
y
ste
ms
wit
h
A
p
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ti
o
n
s
,
v
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l
.
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.
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3
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M.
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ra
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re
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h
t,
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n
d
A.
S
a
lma
n
,
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.
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.
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h
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ste
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h
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5
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sw
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ri,
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le
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m
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sin
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t.
J
.
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6
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.
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h
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li
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d
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u
,
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term
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n
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m
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n
a
d
a
ta se
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,”
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.
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o
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t
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[1
7
]
W.
Ha
rd
i,
W.
A.
K
u
su
m
a
,
a
n
d
S
.
Ba
su
k
i,
“
Clu
ste
ri
n
g
to
p
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ro
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f
d
o
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u
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ts
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sin
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e
a
n
s
a
lg
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rit
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m
:
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stra
li
a
n
Emb
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y
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rta
m
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ia
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s
e
s
2
0
0
6
-
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0
1
6
,
”
Ber
k
.
Ilmu
Per
p
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a
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n
f.
,
v
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l
.
1
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o
.
2
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.
[1
8
]
R.
Ti
b
s
h
ir
a
n
i
,
G
.
Walth
e
r,
a
n
d
T.
Ha
stie,
“
Esti
m
a
ti
n
g
th
e
N
u
m
b
e
r
o
f
Cl
u
ste
rs
in
a
Da
ta
S
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t
Via
t
h
e
G
a
p
S
tatisti
c
s
,”
J
o
u
rn
a
l
o
f
t
h
e
Ro
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l
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ta
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isti
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a
l
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:
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rie
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(S
ta
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0
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2
9
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.
[1
9
]
H.
Jia
,
L.
Wan
g
,
H.
S
o
n
g
,
Q.
M
a
o
,
a
n
d
S
.
Din
g
,
“
A
K
-
AP
Clu
st
e
rin
g
Alg
o
rit
h
m
Ba
se
d
o
n
M
a
n
if
o
ld
S
imilarit
y
M
e
a
su
re
,”
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
telli
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n
t
I
n
fo
rm
a
ti
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n
Pr
o
c
e
ss
in
g
(e
d
s)
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n
telli
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I
n
fo
rm
a
ti
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Pro
c
e
ss
in
g
IX
,
2
0
1
8
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p
p
.
2
0
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2
9
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.
[2
0
]
A.
F
.
M
o
ia
n
e
,
a
n
d
Á.
M
.
L.
M
a
c
h
a
d
o
,
“
E
v
a
lu
a
ti
o
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f
th
e
c
lu
ste
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p
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sid
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o
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p
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ra
m
e
ter
a
n
d
d
a
m
p
in
g
f
a
c
to
r,
”
Bo
letim
d
e
Ciên
c
ia
s
Ge
o
d
é
sic
a
s
,
v
o
l.
24
,
n
o
.
4
,
p
p
.
4
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6
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4
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2
7
.
[2
1
]
A.
M
.
S
e
rd
a
h
a
n
d
W.
M
.
As
h
o
u
r,
“
Clu
ste
ri
n
g
Lar
g
e
S
c
a
le
Da
ta
Ba
se
d
o
n
M
o
d
ifi
e
d
Affi
n
it
y
P
r
o
p
a
g
a
ti
o
n
Alg
o
rit
h
m
,”
J
o
u
rn
a
l
o
f
Arti
fi
c
ia
l
In
telli
g
e
n
c
e
&
S
o
ft
Co
m
p
u
t
in
g
Res
e
a
rc
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,
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l.
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p
.
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-
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5
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6
-
0
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3
.
[2
2
]
L.
Wa
n
g
e
t
a
l.
,
“
An
Im
p
ro
v
e
d
In
teg
ra
ted
Cl
u
ste
rin
g
Lea
rn
in
g
S
tra
teg
y
Ba
se
d
o
n
Th
re
e
-
S
ta
g
e
Affi
n
it
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P
ro
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a
g
a
ti
o
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Alg
o
rit
h
m
with
De
n
sity
P
e
a
k
Op
t
imiz
a
ti
o
n
T
h
e
o
ry
,”
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mp
lex
it
y
,
v
o
l.
2
0
2
1
,
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0
2
1
,
d
o
i:
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1
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5
5
/
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0
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1
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6
6
6
6
1
9
.
[2
3
]
J.
M
e
n
g
,
H.
Ha
o
,
a
n
d
Y.
L
u
a
n
,
“
Clas
sifier
e
n
se
m
b
le
se
lec
ti
o
n
b
a
se
d
o
n
a
ffi
n
it
y
p
r
o
p
a
g
a
ti
o
n
c
lu
ste
r
in
g
,”
J
Bi
o
me
d
In
fo
rm
,
v
o
l
.
60
,
p
p
.
2
3
4
-
2
4
2
,
2
0
1
6
,
d
o
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0
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6
/j
.
jb
i.
2
0
1
6
.
0
2
.
0
1
0
.
[2
4
]
H.
Jia
,
S
.
Din
g
,
L
.
M
e
n
g
,
a
n
d
S
.
F
a
n
,
“
A
d
e
n
sity
-
a
d
a
p
ti
v
e
a
ffin
it
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ro
p
a
g
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ti
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ste
rin
g
a
lg
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rit
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m
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se
d
o
n
sp
e
c
tral
d
ime
n
sio
n
re
d
u
c
ti
o
n
,”
N
e
u
ra
l
Co
m
p
u
t
&
A
p
p
li
c
,
v
o
l.
2
5
,
pp.
1
5
5
7
-
1
5
6
7
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0
1
4
,
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o
i
:
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0
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5
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1
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4
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-
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.
[2
5
]
X.
Zh
a
o
a
n
d
W. X
.
Xu
,
“
An
e
x
te
n
d
e
d
a
ffi
n
it
y
p
ro
p
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g
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ti
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ste
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se
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o
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d
if
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re
n
t
d
a
t
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d
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sity
ty
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e
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,”
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mp
u
t
.
In
tell.
Ne
u
r
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sc
i.
,
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p
.
1
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o
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8
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5
7
.
[2
6
]
X.
Zh
a
n
g
,
W.
Wan
g
,
K.
N
ø
rv
å
g
,
a
n
d
M
.
S
e
b
a
g
,
“
K
-
AP:
G
e
n
e
ra
ti
n
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sp
e
c
ifi
e
d
K
c
lu
ste
rs
b
y
e
fficie
n
t
Affi
n
it
y
P
ro
p
a
g
a
ti
o
n
,
”
P
r
o
c
.
-
IEE
E
In
t.
Co
n
f.
Da
ta
M
in
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n
g
,
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o
.
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p
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.
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[2
7
]
M
.
J.
Bu
n
k
e
rs,
J.
R.
M
il
ler,
a
n
d
A.
T.
De
Ga
e
tan
o
,
“
De
fin
it
io
n
o
f
c
li
m
a
te
re
g
io
n
s
in
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h
e
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rt
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g
a
n
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jec
ti
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l
u
ste
r
m
o
d
i
fica
ti
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n
tec
h
n
i
q
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e
,
”
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o
u
rn
a
l
o
f
Cli
ma
te
,
v
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l.
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1
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CO;
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.
[2
8
]
C.
J.
Ve
e
n
m
a
n
,
M
.
J.
T
.
Re
i
n
d
e
rs an
d
E.
Ba
c
k
e
r,
“
A M
a
x
imu
m
Va
r
ian
c
e
Clu
ste
r
Al
g
o
r
it
h
m
,”
in
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
a
tt
e
rn
An
a
lys
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a
n
d
M
a
c
h
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n
telli
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e
n
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