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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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A
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Un
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(
Th
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a
k
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P
a
mu
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)
1311
Featu
r
e
s
elec
tio
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is
v
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in
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p
tim
izin
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cl
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s
ter
in
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esp
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s
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m
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t r
elev
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t a
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etain
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Mo
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d
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r
tin
g
d
ata
-
d
r
iv
e
n
d
ec
is
io
n
-
m
ak
in
g
in
h
ig
h
er
ed
u
ca
tio
n
in
s
titu
tio
n
s
.
Fo
llo
win
g
a
th
o
r
o
u
g
h
r
e
v
iew
o
f
r
elev
a
n
t
liter
atu
r
e
an
d
r
ese
ar
ch
s
tu
d
ies,
s
ev
er
al
in
v
esti
g
a
tio
n
s
h
av
e
ex
p
lo
r
ed
th
e
ap
p
licatio
n
o
f
c
lu
s
ter
i
n
g
tech
n
iq
u
es
alo
n
g
s
id
e
v
ar
io
u
s
d
atasets
to
en
h
an
ce
s
tu
d
en
t
ad
m
is
s
io
n
an
aly
s
is
.
T
h
ese
s
tu
d
ies
h
av
e
d
em
o
n
s
tr
ated
th
e
ef
f
ec
tiv
e
n
e
s
s
o
f
clu
s
ter
in
g
m
eth
o
d
s
in
s
eg
m
en
tin
g
s
tu
d
en
t
p
o
p
u
latio
n
s
b
ased
o
n
ac
ad
e
m
ic
an
d
d
em
o
g
r
ap
h
ic
attr
ib
u
tes.
T
ab
le
1
[
1
6
]
-
[
3
0
]
(
i
n
A
p
p
en
d
i
x
)
p
r
o
v
i
d
es
a
co
m
p
r
eh
e
n
s
iv
e
s
u
m
m
ar
y
o
f
t
h
ese
f
in
d
in
g
s
,
h
ig
h
lig
h
tin
g
k
ey
m
eth
o
d
o
l
o
g
ies an
d
r
esu
lts
f
r
o
m
p
r
io
r
r
esear
ch
.
Sev
er
al
s
tu
d
ies
h
av
e
e
x
p
lo
r
ed
d
ata
m
i
n
in
g
tec
h
n
iq
u
es
in
ed
u
ca
tio
n
,
p
ar
ticu
lar
ly
in
s
tu
d
en
t
p
er
f
o
r
m
an
ce
p
r
e
d
ictio
n
an
d
d
r
o
p
o
u
t
an
aly
s
is
.
Ho
wev
er
,
g
a
p
s
r
em
ain
in
ap
p
ly
in
g
clu
s
ter
in
g
tech
n
iq
u
es
f
o
r
ad
m
is
s
io
n
d
ata
an
aly
s
is
,
as
p
r
io
r
s
tu
d
ies
p
r
e
d
o
m
in
a
n
tly
f
o
cu
s
o
n
p
r
ed
ictin
g
s
tu
d
en
t
ac
a
d
em
ic
p
er
f
o
r
m
a
n
ce
r
ath
er
th
an
o
p
tim
izin
g
s
tu
d
e
n
t
r
ec
r
u
itm
en
t
s
tr
ateg
ies.
Ad
d
itio
n
ally
,
f
ew
r
esear
ch
ef
f
o
r
t
s
h
av
e
co
m
p
ar
ed
m
u
ltip
le
clu
s
ter
in
g
tech
n
iq
u
es
s
u
ch
as
K
-
m
ea
n
s
,
DB
SC
AN,
an
d
Hier
ar
ch
ical
C
lu
s
ter
in
g
with
in
r
ea
l
s
tu
d
en
t
ad
m
is
s
io
n
d
atasets
.
Mo
r
eo
v
er
,
th
e
lack
o
f
p
r
o
p
e
r
v
alid
atio
n
m
etr
ics,
s
u
ch
as
th
e
d
av
ies
-
b
o
u
ld
in
in
d
ex
(
DB
I
)
,
lim
its
th
e
ab
ilit
y
to
d
eter
m
in
e
th
e
m
o
s
t
ef
f
ec
tiv
e
clu
s
ter
in
g
ap
p
r
o
ac
h
f
o
r
s
tu
d
en
t
s
eg
m
en
tatio
n
.
Giv
en
th
ese
g
ap
s
,
th
er
e
is
a
p
r
ess
in
g
n
ee
d
to
in
v
esti
g
ate
a
d
v
an
ce
d
cl
u
s
ter
in
g
ap
p
r
o
ac
h
es
f
o
r
s
tu
d
en
t
ad
m
is
s
io
n
d
ata
to
en
h
an
ce
d
ec
is
io
n
-
m
ak
in
g
p
r
o
c
ess
es in
h
ig
h
er
ed
u
ca
tio
n
in
s
titu
tio
n
s
.
T
h
is
r
esear
ch
m
ak
es
th
e
f
o
llo
win
g
k
ey
c
o
n
tr
ib
u
ti
o
n
s
to
th
e
f
ield
o
f
s
tu
d
en
t
a
d
m
is
s
io
n
d
at
a
an
aly
s
is
an
d
clu
s
ter
in
g
:
−
Dev
elo
p
a
n
o
v
el
clu
s
ter
in
g
f
r
am
ewo
r
k
in
teg
r
ati
n
g
PC
A
f
o
r
f
ea
tu
r
e
s
elec
tio
n
an
d
DB
I
f
o
r
v
alid
atio
n
t
o
im
p
r
o
v
e
cl
u
s
ter
in
g
ac
cu
r
ac
y
a
n
d
in
ter
p
r
etab
ilit
y
.
−
C
o
n
d
u
cts
a
co
m
p
ar
ativ
e
ev
alu
atio
n
o
f
clu
s
ter
in
g
tech
n
i
q
u
es,
in
clu
d
in
g
K
-
m
ea
n
s
,
DB
S
C
AN,
an
d
Hier
ar
ch
ical
C
lu
s
ter
in
g
.
−
Ap
p
lies
clu
s
ter
in
g
tech
n
iq
u
es
to
a
d
m
is
s
io
n
d
ata
f
r
o
m
Yal
a
R
ajab
h
at
Un
iv
e
r
s
ity
to
p
r
o
v
id
e
p
r
ac
tical
in
s
ig
h
ts
f
o
r
r
ec
r
u
itm
en
t a
n
d
ac
ad
em
ic
p
lan
n
i
n
g
.
−
Su
p
p
o
r
ts
d
ata
-
d
r
iv
en
d
ec
is
io
n
-
m
ak
in
g
in
u
n
iv
e
r
s
ity
ad
m
i
s
s
io
n
s
b
y
f
ac
ilit
atin
g
tar
g
eted
r
ec
r
u
itm
en
t
s
tr
ateg
ies b
ased
o
n
well
-
d
ef
in
ed
s
tu
d
en
t c
lu
s
ter
s
.
T
h
e
r
em
ain
d
er
o
f
th
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws:
Se
ctio
n
2
:
Me
th
o
d
o
l
o
g
y
–
Des
cr
ib
es
d
ata
co
llectio
n
,
p
r
e
p
r
o
ce
s
s
in
g
,
an
d
f
ea
tu
r
e
s
elec
tio
n
u
s
in
g
PC
A
.
I
t
ex
p
lain
s
clu
s
ter
in
g
tech
n
iq
u
es,
in
clu
d
i
n
g
K
-
m
ea
n
s
,
DB
SC
A
N,
an
d
Hier
ar
ch
ical
C
lu
s
ter
in
g
,
an
d
in
tr
o
d
u
ce
s
th
e
DB
I
f
o
r
clu
s
ter
in
g
v
al
id
atio
n
.
Sectio
n
3
:
R
esu
lts
an
d
An
aly
s
is
–
Pre
s
en
ts
clu
s
ter
in
g
r
esu
lts
,
ev
alu
at
es
p
er
f
o
r
m
an
ce
u
s
in
g
DB
I
s
c
o
r
es,
an
d
v
is
u
alize
s
clu
s
ter
in
g
o
u
tco
m
es.
Sectio
n
4
:
Dis
cu
s
s
io
n
–
I
n
ter
p
r
ets
f
in
d
in
g
s
,
ass
ess
es
clu
s
ter
in
g
p
er
f
o
r
m
an
ce
,
a
n
d
ex
p
lo
r
es
its
p
r
ac
tical
ap
p
licatio
n
s
in
s
tu
d
en
t
ad
m
is
s
io
n
s
.
Sectio
n
5
:
C
o
n
clu
s
io
n
an
d
Fu
tu
r
e
W
o
r
k
–
Su
m
m
ar
izes k
ey
r
esear
ch
f
in
d
in
g
s
,
d
is
cu
s
s
es st
u
d
y
lim
itatio
n
s
,
an
d
p
r
o
p
o
s
es d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
r
esear
ch
m
et
h
o
d
o
lo
g
y
u
s
ed
to
an
aly
ze
an
d
clu
s
te
r
s
tu
d
en
t
ad
m
is
s
io
n
d
ata
at
Yala
R
ajab
h
at
Un
iv
e
r
s
ity
.
T
h
e
d
ata
was
p
r
e
p
r
o
ce
s
s
ed
,
cl
u
s
ter
ed
,
an
d
e
v
alu
ated
f
o
r
o
p
t
im
al
s
eg
m
en
tatio
n
.
T
h
e
m
eth
o
d
o
lo
g
y
f
o
llo
ws a
s
y
s
tem
atic
ap
p
r
o
ac
h
,
as illu
s
tr
at
ed
in
Fig
u
r
e
1
.
T
h
e
f
o
llo
win
g
ap
p
licatio
n
o
f
t
h
e
d
ata
m
in
in
g
clu
s
ter
in
g
m
o
d
el
o
n
an
al
y
zin
g
an
d
clu
s
ter
in
g
s
tu
d
en
ts
wh
o
ch
o
o
s
e
to
Stu
d
y
at
Yala
R
ajab
h
at
Un
iv
er
s
ity
as f
o
llo
ws:
2
.
1
.
Da
t
a
c
o
llect
io
n
Stu
d
en
t
ad
m
is
s
io
n
h
is
to
r
y
d
at
a
was
co
llected
f
r
o
m
th
e
E
d
u
ca
tio
n
al
Ser
v
ices
Div
is
io
n
,
Of
f
ice
o
f
th
e
Pre
s
id
en
t,
Yala
R
ajab
h
at
U
n
iv
er
s
ity
.
T
h
e
d
ataset
c
o
m
p
r
is
e
s
r
ec
o
r
d
s
f
r
o
m
th
e
ac
ad
e
m
ic
y
ea
r
s
2
0
1
9
-
2
0
2
3
,
to
talin
g
1
3
,
4
3
5
d
ata
en
tr
ies.
Key
attr
ib
u
tes
in
clu
d
e
s
ex
,
r
elig
io
n
,
h
o
m
et
o
wn
p
r
o
v
in
ce
,
s
ch
o
o
l,
s
ch
o
o
l
p
la
n
,
g
r
ad
e
p
o
i
n
t
av
er
ag
e
(
GPA)
,
p
r
o
g
r
am
,
an
d
f
ac
u
lty
.
T
o
en
s
u
r
e
p
r
i
v
ac
y
,
all
id
en
tify
in
g
in
f
o
r
m
atio
n
was
an
o
n
y
m
ized
.
T
h
e
d
ata
was e
x
t
r
ac
ted
f
r
o
m
in
s
titu
tio
n
al
d
atab
ases
to
m
ain
tain
ac
cu
r
ac
y
a
n
d
in
teg
r
ity
.
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.
39
,
No
.
2
,
Au
g
u
s
t
20
25
:
1
3
1
0
-
1
325
1312
Fig
u
r
e
1
.
R
esear
ch
m
eth
o
d
o
l
o
g
y
2.
2
.
Da
t
a
prepro
ce
s
s
ing
Data
p
r
ep
r
o
ce
s
s
in
g
en
s
u
r
es
t
h
e
d
ataset
is
clea
n
,
s
tr
u
ctu
r
e
d
,
an
d
f
o
r
m
atted
co
r
r
ec
tly
f
o
r
clu
s
ter
in
g
an
aly
s
is
.
T
h
is
s
tep
in
v
o
lv
es
n
o
m
in
al
d
ata
en
c
o
d
in
g
,
clea
n
in
g
,
an
d
s
tan
d
ar
d
izatio
n
to
im
p
r
o
v
e
p
r
o
ce
s
s
in
g
ef
f
icien
cy
an
d
co
n
s
is
ten
cy
.
2
.
2
.
1
.
No
m
ina
l
da
t
a
enco
din
g
No
m
in
al
d
ata
e
n
co
d
i
n
g
is
es
s
en
tial
f
o
r
c
o
n
v
er
tin
g
ca
teg
o
r
ical
v
ar
iab
les
in
to
n
u
m
er
ica
l
f
o
r
m
ats
s
u
itab
le
f
o
r
clu
s
ter
in
g
alg
o
r
ith
m
s
.
T
h
is
s
tu
d
y
'
s
ca
teg
o
r
ical
v
ar
iab
les,
s
u
ch
as
s
ch
o
o
l
ty
p
e,
p
r
o
v
in
ce
,
an
d
s
tu
d
y
p
lan
,
wer
e
tr
an
s
f
o
r
m
ed
u
s
in
g
n
u
m
er
ic
co
d
es.
T
h
is
c
o
n
v
er
s
io
n
allo
wed
al
g
o
r
ith
m
s
t
o
p
r
o
ce
s
s
t
h
e
d
ata
ef
f
icien
tly
with
o
u
t m
is
in
ter
p
r
e
tatio
n
d
u
e
to
ca
teg
o
r
ical
v
alu
e
s
.
2
.
2
.
2
.
Da
t
a
clea
nin
g
Data
clea
n
in
g
in
v
o
l
v
es
r
em
o
v
in
g
d
u
p
licate
r
ec
o
r
d
s
,
h
a
n
d
lin
g
m
is
s
in
g
v
alu
es,
an
d
r
eso
lv
in
g
in
co
n
s
is
ten
cies.
Attr
ib
u
tes
s
u
ch
as
s
ex
a
n
d
r
elig
io
n
wer
e
o
m
itted
to
r
e
d
u
ce
n
o
is
e
a
n
d
en
h
an
ce
th
e
r
elev
a
n
ce
o
f
clu
s
ter
in
g
.
T
h
e
d
ataset
was
f
u
r
th
er
ex
a
m
in
ed
f
o
r
o
u
tlier
s
an
d
er
r
o
n
eo
u
s
en
tr
ies,
wh
ich
wer
e
eith
e
r
c
o
r
r
e
c
t
e
d
o
r
r
e
m
o
v
e
d
b
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t
a
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s
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d
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a
.
T
h
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e
f
i
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d
d
a
t
as
e
t
i
s
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u
m
m
a
r
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z
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d
i
n
T
a
b
l
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2
,
s
h
o
win
g
en
co
d
ed
a
n
d
clea
n
e
d
attr
ib
u
tes.
T
ab
le
2
.
No
m
i
n
al
d
ata
en
c
o
d
i
n
g
an
d
clea
n
ed
d
ataset
No
S
c
h
C
o
d
e
S
c
h
P
r
o
C
o
d
e
S
c
h
T
y
p
e
C
o
d
e
S
c
h
P
l
a
n
C
o
d
e
G
p
a
x
C
o
d
e
P
r
o
g
r
a
m
F
a
c
u
l
t
y
c
o
d
e
1
1
95
11
1
3
2
1
2
1
95
11
2
4
2
1
3
3
91
1
2
3
2
1
4
4
94
1
2
3
2
1
5
5
96
11
2
3
2
1
6
6
94
11
2
4
2
1
7
7
95
11
2
3
2
1
8
8
91
11
2
4
2
1
2
.
2
.
3
.
Da
t
a
s
t
a
nd
a
rdiza
t
io
n
S
t
a
n
d
a
r
d
i
z
e
t
h
e
d
a
t
a
t
o
e
n
s
u
r
e
t
h
a
t
a
l
l
n
u
m
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r
i
c
a
l
v
a
r
i
a
b
l
es
a
r
e
b
r
o
u
g
h
t
t
o
a
u
n
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f
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r
m
s
c
a
l
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,
w
h
i
c
h
m
a
y
a
l
s
o
i
n
v
o
l
v
e
e
n
c
o
d
i
n
g
c
at
e
g
o
r
i
c
a
l
v
a
r
ia
b
l
es
.
T
h
is
s
t
a
n
d
a
r
d
iz
a
t
i
o
n
p
r
o
c
e
s
s
e
n
t
a
il
s
t
r
a
n
s
f
o
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m
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n
g
t
h
e
d
a
t
a
b
y
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u
b
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r
a
c
t
i
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h
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m
ea
n
a
n
d
d
i
v
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d
i
n
g
i
t
b
y
t
h
e
s
t
a
n
d
a
r
d
d
e
v
i
a
ti
o
n
.
T
h
i
s
s
te
p
i
s
c
r
u
c
ia
l
t
o
m
it
i
g
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t
e
b
i
a
s
e
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t
h
at
c
o
u
l
d
a
r
i
s
e
w
h
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n
v
a
r
i
a
b
l
es
w
it
h
l
a
r
g
er
s
c
a
l
es
d
is
p
r
o
p
o
r
t
i
o
n
a
t
e
l
y
a
f
f
ec
t
t
h
e
o
u
t
c
o
m
es
o
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h
e
c
l
u
s
t
e
r
i
n
g
a
l
g
o
r
i
t
h
m
.
2
.
2
.
4
.
Det
er
m
ini
ng
t
he
nu
m
ber
o
f
clus
t
er
s
I
d
en
tify
in
g
th
e
o
p
tim
al
n
u
m
b
e
r
o
f
cl
u
s
ter
s
f
o
r
K
-
m
ea
n
s
an
d
h
ier
ar
ch
ical
clu
s
ter
in
g
alg
o
r
it
h
m
s
u
s
in
g
th
e
E
lb
o
w
Me
th
o
d
[
2
2
]
,
[
3
1
]
.
T
h
e
v
alu
e
o
f
k
I
d
en
tifie
s
th
e
o
p
tim
al
n
u
m
b
er
o
f
clu
s
ter
s
th
at
b
est
r
ep
r
esen
t
th
e
u
n
d
er
ly
i
n
g
p
atter
n
s
in
th
e
d
at
a
[
2
2
]
.
I
m
p
lem
en
t
m
eth
o
d
s
li
k
e
th
e
E
lb
o
w
Me
th
o
d
,
wh
ich
i
n
v
o
lv
es
p
lo
ttin
g
th
e
s
u
m
o
f
s
q
u
ar
ed
d
is
tan
ce
s
f
r
o
m
ea
ch
p
o
in
t
t
o
its
ass
ig
n
ed
c
en
ter
an
d
ch
o
o
s
in
g
th
e
p
o
in
t
wh
er
e
im
p
r
o
v
em
e
n
ts
b
ec
o
m
e
m
ar
g
in
al.
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
A
n
a
lyzi
n
g
a
n
d
clu
s
teri
n
g
s
tu
d
en
ts
a
d
mis
s
io
n
d
a
ta
in
Ya
l
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R
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ja
b
h
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t
Un
ivers
ity
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(
Th
a
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a
k
o
r
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P
a
mu
th
a
)
1313
2
.
3
.
F
e
a
t
ure
s
elec
t
io
n
Featu
r
e
s
elec
tio
n
en
h
an
ce
s
cl
u
s
ter
in
g
ac
cu
r
ac
y
b
y
r
e
d
u
cin
g
d
im
en
s
io
n
alit
y
an
d
r
etain
in
g
o
n
ly
th
e
m
o
s
t
r
elev
an
t
attr
ib
u
tes
[
2
5
]
.
PC
A
was
ap
p
lied
to
id
en
tify
th
e
k
ey
f
ea
tu
r
es
co
n
tr
i
b
u
tin
g
to
v
ar
ian
ce
i
n
th
e
d
ataset
[
2
6
]
.
T
h
is
wid
ely
u
s
ed
d
im
en
s
io
n
ality
r
ed
u
ctio
n
tech
n
iq
u
e
tr
an
s
f
o
r
m
s
h
ig
h
-
d
im
e
n
s
io
n
al
d
ata
in
to
a
lo
wer
-
d
im
en
s
io
n
al
s
p
ac
e
wh
i
le
p
r
eser
v
in
g
as
m
u
ch
v
ar
ia
n
ce
as
p
o
s
s
ib
le
[
2
7
]
.
I
n
th
e
co
n
tex
t
o
f
s
tu
d
en
t
ad
m
is
s
io
n
d
ata
a
n
aly
s
is
,
PC
A
h
elp
s
s
elec
t
th
e
m
o
s
t
im
p
o
r
tan
t
f
ea
tu
r
es
co
n
tr
i
b
u
tin
g
to
cl
u
s
ter
in
g
,
e
n
s
u
r
in
g
ef
f
icien
t
d
ata
p
r
o
ce
s
s
in
g
an
d
im
p
r
o
v
ed
clu
s
ter
q
u
ality
[
2
8
]
.
B
y
ap
p
l
y
in
g
PC
A,
th
i
s
s
tu
d
y
ef
f
ec
tiv
ely
id
en
tifie
d
th
e
m
o
s
t
im
p
o
r
tan
t
s
tu
d
en
t
attr
ib
u
tes
co
n
tr
ib
u
tin
g
to
m
ea
n
in
g
f
u
l
clu
s
ter
in
g
.
T
h
e
m
eth
o
d
r
ed
u
ce
d
d
ata
d
im
en
s
io
n
ality
,
im
p
r
o
v
ed
clu
s
ter
in
g
ac
cu
r
ac
y
,
a
n
d
f
ac
ilit
ated
b
etter
s
eg
m
en
tat
io
n
o
f
s
tu
d
en
ts
f
o
r
tar
g
et
ed
ad
m
is
s
io
n
s
tr
ateg
ies
[
2
8
]
.
2
.
4
.
Clus
t
er
ing
T
h
r
e
e
c
l
u
s
t
e
r
i
n
g
te
c
h
n
i
q
u
e
s
we
r
e
a
p
p
l
i
e
d
t
o
s
e
g
m
e
n
t
t
h
e
s
t
u
d
e
n
t
a
d
m
i
s
s
i
o
n
d
at
a
:
K
-
m
e
a
n
s
,
D
B
SC
A
N,
a
n
d
H
i
e
r
a
r
c
h
i
c
al
C
l
u
s
te
r
i
n
g
.
E
a
c
h
a
l
g
o
r
i
t
h
m
p
r
o
v
i
d
e
s
d
is
t
i
n
ct
a
d
v
a
n
t
a
g
e
s
,
a
ll
o
w
i
n
g
f
o
r
c
o
m
p
r
e
h
e
n
s
i
v
e
a
n
a
l
y
s
is
:
2
.
4
.
1
.
K
-
m
ea
ns
clus
t
er
ing
K
-
m
ea
n
s
clu
s
ter
in
g
is
o
n
e
o
f
th
e
m
o
s
t
wid
ely
u
s
ed
u
n
s
u
p
er
v
is
ed
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
f
o
r
p
ar
titi
o
n
in
g
a
d
ataset
in
to
K
d
is
tin
ct,
n
o
n
-
o
v
er
la
p
p
in
g
clu
s
ter
s
.
I
t
is
p
ar
ticu
la
r
ly
e
f
f
ec
tiv
e
f
o
r
well
-
s
ep
ar
ated
an
d
s
p
h
er
ical
clu
s
ter
s
,
m
ak
in
g
it
a
p
o
p
u
lar
ch
o
ice
in
ed
u
c
atio
n
al
d
ata
m
in
in
g
,
in
clu
d
in
g
s
tu
d
en
t
ad
m
is
s
io
n
an
aly
s
is
.
T
h
e
alg
o
r
ith
m
m
in
i
m
izes
in
tr
a
-
clu
s
ter
v
ar
ian
ce
wh
ile
m
ax
im
izin
g
in
ter
-
clu
s
ter
s
ep
ar
atio
n
,
en
s
u
r
in
g
th
at
d
ata
p
o
in
ts
with
in
a
cl
u
s
ter
ar
e
m
o
r
e
s
im
ilar
th
an
th
o
s
e
in
d
if
f
er
e
n
t c
lu
s
ter
s
[
1
6
]
,
[
2
3
]
.
2
.
4
.
2
.
DB
SCAN
DB
S
C
AN
i
s
a
d
en
s
ity
-
b
ased
clu
s
ter
in
g
alg
o
r
ith
m
th
at
g
r
o
u
p
s
to
g
eth
er
d
ata
p
o
in
ts
th
at
ar
e
clo
s
ely
p
ac
k
ed
wh
ile
i
d
en
tify
in
g
p
o
in
ts
th
at
lie
in
lo
w
-
d
e
n
s
ity
r
e
g
io
n
s
as
o
u
tlier
s
[
2
3
]
.
U
n
lik
e
K
-
m
ea
n
s
,
wh
ich
r
eq
u
ir
e
s
p
ec
if
y
i
n
g
th
e
n
u
m
b
e
r
o
f
clu
s
ter
s
b
ef
o
r
e
h
an
d
,
DB
SC
AN
au
to
m
atica
lly
d
eter
m
in
es
th
e
n
u
m
b
e
r
o
f
clu
s
ter
s
b
ased
o
n
d
ata
d
is
tr
ib
u
tio
n
[
3
2
]
.
T
h
is
m
ak
es
it
esp
ec
ially
ef
f
ec
tiv
e
f
o
r
d
atase
ts
with
clu
s
ter
s
o
f
ar
b
itra
r
y
s
h
ap
e
an
d
v
ar
y
i
n
g
d
en
s
ities
,
in
clu
d
in
g
s
tu
d
en
t
ad
m
is
s
io
n
d
ata,
wh
er
e
s
tu
d
en
t
g
r
o
u
p
s
m
ay
n
o
t
h
av
e
clea
r
,
s
p
h
er
ical
d
is
tr
ib
u
tio
n
s
[
2
4
]
.
2
.
4
.
3
.
H
iera
rc
hica
l
clus
t
er
ing
Hier
ar
ch
ical
C
lu
s
ter
in
g
is
a
p
o
wer
f
u
l
u
n
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
i
n
g
tec
h
n
iq
u
e
u
s
e
d
to
g
r
o
u
p
s
im
ilar
d
ata
p
o
in
ts
in
t
o
a
t
r
ee
-
lik
e
s
tr
u
ctu
r
e,
k
n
o
wn
as
a
d
e
n
d
r
o
g
r
am
[
2
3
]
.
Un
lik
e
K
-
m
ea
n
s
an
d
DB
SC
AN,
wh
ich
r
e
q
u
ir
e
p
r
ed
e
f
in
ed
p
ar
a
m
eter
s
f
o
r
th
e
n
u
m
b
er
o
f
clu
s
ter
s
,
Hier
ar
ch
ical
C
lu
s
ter
in
g
f
o
r
m
s
a
h
ier
ar
ch
y
o
f
n
ested
clu
s
ter
s
,
allo
win
g
f
lex
ib
ilit
y
in
clu
s
ter
s
elec
tio
n
at
d
i
f
f
er
en
t
lev
els.
T
h
is
m
eth
o
d
is
p
ar
ticu
lar
ly
u
s
ef
u
l
in
s
tu
d
en
t
ad
m
is
s
io
n
an
aly
s
is
,
as
it
h
elp
s
u
n
iv
er
s
ities
id
en
tify
r
elatio
n
s
h
ip
s
b
etwe
en
s
tu
d
e
n
t
g
r
o
u
p
s
b
ased
o
n
ac
ad
em
ic
b
ac
k
g
r
o
u
n
d
s
,
g
eo
g
r
ap
h
ical
r
eg
i
o
n
s
,
an
d
p
r
o
g
r
am
ch
o
ices.
B
y
v
is
u
alizin
g
t
h
e
cl
u
s
ter
in
g
p
r
o
ce
s
s
as
a
tr
ee
,
in
s
titu
tio
n
s
ca
n
ex
p
lo
r
e
s
tu
d
en
t similar
ities
at
v
ar
io
u
s
le
v
els o
f
g
r
a
n
u
lar
ity
[
2
4
]
.
2
.
5
.
M
o
del
ev
a
lua
t
i
o
n
Mo
d
el
ev
alu
atio
n
u
s
in
g
th
e
D
B
I
m
ea
s
u
r
es
h
o
w
s
im
ilar
an
o
b
ject
is
to
its
o
wn
clu
s
ter
co
m
p
ar
ed
to
o
th
er
clu
s
ter
s
,
en
s
u
r
in
g
a
g
o
o
d
clu
s
ter
in
g
co
n
f
ig
u
r
atio
n
.
T
h
e
DB
I
ev
alu
ates
clu
s
ter
in
g
q
u
ality
b
y
ass
ess
in
g
b
o
th
th
e
co
m
p
ac
tn
ess
an
d
s
ep
ar
atio
n
o
f
clu
s
ter
s
,
m
ea
s
u
r
in
g
h
o
w
well
th
ey
ar
e
d
is
tin
ct
f
r
o
m
ea
ch
o
th
e
r
an
d
co
m
p
ac
t
with
in
th
em
s
elv
es
[
2
9
]
,
[
3
2
]
.
I
t
ca
lcu
lates
th
e
av
e
r
ag
e
s
im
ilar
ity
r
atio
b
etwe
en
e
ac
h
clu
s
ter
an
d
its
m
o
s
t
s
im
ilar
n
eig
h
b
o
r
in
g
cl
u
s
ter
,
co
n
s
id
er
in
g
b
o
th
th
e
i
n
tr
a
-
clu
s
ter
d
is
tan
ce
(
th
e
a
v
er
ag
e
d
is
tan
ce
b
etwe
en
p
o
in
ts
with
in
th
e
s
am
e
clu
s
ter
)
an
d
th
e
in
ter
-
clu
s
ter
d
is
tan
c
e
(
th
e
d
is
tan
ce
b
etwe
en
th
e
ce
n
tr
o
id
s
o
f
d
if
f
er
e
n
t
clu
s
ter
s
)
.
A
lo
wer
DB
I
v
alu
e
in
d
icate
s
b
etter
clu
s
ter
in
g
p
er
f
o
r
m
an
ce
,
ch
ar
ac
ter
ized
b
y
co
m
p
ac
t
clu
s
ter
s
th
at
ar
e
well
-
s
ep
ar
ated
f
r
o
m
ea
ch
o
th
er
,
wh
ile
a
h
i
g
h
er
DB
I
v
alu
e
s
u
g
g
ests
p
o
o
r
er
cl
u
s
ter
in
g
w
ith
o
v
er
lap
p
in
g
a
n
d
less
d
i
s
tin
ct
clu
s
ter
s
[
2
9
]
,
[
3
0
]
.
T
h
is
in
d
ex
is
p
ar
ticu
lar
ly
u
s
ef
u
l
f
o
r
co
m
p
ar
in
g
d
if
f
er
en
t
cl
u
s
ter
in
g
r
esu
lts
an
d
d
eter
m
in
in
g
th
e
o
p
tim
al
n
u
m
b
er
o
f
clu
s
ter
s
with
in
a
d
ataset,
m
ak
in
g
it
a
v
alu
a
b
le
to
o
l
f
o
r
an
aly
zi
n
g
a
n
d
v
alid
atin
g
clu
s
ter
in
g
al
g
o
r
ith
m
s
.
2
.
6
.
T
o
o
l
a
nd
da
t
a
v
is
ua
liza
t
io
n
T
h
e
Py
th
o
n
p
r
o
g
r
am
m
in
g
la
n
g
u
ag
e
was
u
s
ed
ex
ten
s
iv
ely
f
o
r
d
a
ta
p
r
ep
r
o
ce
s
s
in
g
,
cl
u
s
ter
in
g
,
a
n
d
an
aly
s
is
d
u
e
to
its
p
o
wer
f
u
l
li
b
r
ar
ies
s
u
ch
as
Pan
d
as
f
o
r
d
at
a
m
an
ip
u
latio
n
,
Scik
it
-
lear
n
f
o
r
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
an
d
Ma
tp
lo
tlib
f
o
r
d
ata
v
is
u
aliza
tio
n
.
C
lu
s
ter
in
g
alg
o
r
ith
m
s
,
in
clu
d
i
n
g
K
-
m
ea
n
s
,
Hier
ar
ch
ical
C
lu
s
t
er
in
g
,
an
d
DB
SC
AN,
we
r
e
im
p
lem
en
ted
in
Py
th
o
n
to
s
eg
m
en
t
th
e
s
tu
d
en
t
d
ata
in
t
o
m
ea
n
in
g
f
u
l
clu
s
ter
s
.
L
o
o
k
er
Stu
d
io
was
u
tili
ze
d
t
o
cr
ea
te
co
m
p
r
e
h
en
s
iv
e
v
is
u
a
l
r
ep
r
esen
tatio
n
s
o
f
b
o
th
th
e
d
escr
ip
tiv
e
an
aly
s
is
an
d
th
e
clu
s
ter
in
g
r
esu
lts
.
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.
39
,
No
.
2
,
Au
g
u
s
t
20
25
:
1
3
1
0
-
1
325
1314
T
h
e
an
aly
s
is
in
clu
d
e
d
d
ata
clea
n
in
g
,
s
tan
d
a
r
d
izatio
n
,
an
d
PC
A
to
p
r
e
p
ar
e
d
ata
f
o
r
clu
s
ter
in
g
.
W
e
em
p
lo
y
ed
K
-
m
ea
n
s
,
Hier
ar
ch
ical,
an
d
DB
SC
AN
alg
o
r
ith
m
s
,
ev
alu
atin
g
clu
s
ter
in
g
q
u
ality
with
th
e
DB
I
.
T
h
is
m
eth
o
d
o
lo
g
ical
f
r
am
ew
o
r
k
e
n
s
u
r
es
a
r
o
b
u
s
t
an
aly
s
is
an
d
a
clea
r
u
n
d
er
s
tan
d
i
n
g
o
f
d
ata
p
atter
n
s
a
n
d
f
ac
ilit
ates p
r
ec
is
e
m
ar
k
etin
g
a
n
d
s
tr
ateg
ic
p
lan
n
in
g
.
3.
RE
SU
L
T
S
T
h
e
th
o
r
o
u
g
h
a
n
aly
s
is
an
d
clu
s
ter
in
g
o
f
s
tu
d
en
t
ad
m
is
s
io
n
d
ata
at
Yala
R
ajab
h
at
Un
iv
e
r
s
ity
h
av
e
p
r
o
d
u
ce
d
k
e
y
in
s
ig
h
ts
th
at
ar
e
s
et
to
en
h
an
ce
th
e
u
n
iv
er
s
ity
'
s
r
ec
r
u
itm
en
t
s
tr
ateg
ies
an
d
s
tu
d
en
t
en
g
ag
em
en
t.
T
h
is
s
ec
tio
n
d
etails
th
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
o
f
u
s
in
g
v
ar
io
u
s
clu
s
ter
in
g
alg
o
r
ith
m
s
—
K
-
m
ea
n
s
,
Hier
ar
ch
ical
C
lu
s
ter
in
g
,
an
d
DB
SC
AN
—
to
ca
teg
o
r
ize
ad
m
is
s
io
n
d
ata
b
y
ed
u
ca
tio
n
al
in
s
titu
tio
n
,
g
eo
g
r
a
p
h
ic
lo
ca
tio
n
,
an
d
ch
o
s
en
p
r
o
g
r
am
s
.
T
h
ese
m
eth
o
d
s
h
av
e
s
u
cc
ess
f
u
lly
h
ig
h
li
g
h
ted
v
ar
io
u
s
s
tu
d
en
t
p
r
o
f
iles
,
wh
ich
ar
e
clea
r
ly
illu
s
tr
ated
th
r
o
u
g
h
f
ig
u
r
es a
n
d
tab
les,
s
h
o
wca
s
in
g
th
e
ef
f
ec
ti
v
en
ess
o
f
th
e
clu
s
ter
in
g
p
r
o
ce
s
s
.
3
.
1
.
Descript
iv
e
a
na
ly
s
is
T
h
is
s
ec
tio
n
p
r
o
v
id
es
a
g
en
er
a
l
o
v
er
v
iew
o
f
th
e
s
tu
d
e
n
t
p
o
p
u
latio
n
at
Yala
R
ajab
h
at
Un
iv
e
r
s
ity
f
r
o
m
th
e
ac
ad
em
ic
y
ea
r
s
2
0
1
9
t
o
2
0
2
3
,
w
h
ich
in
cl
u
d
es
a
to
tal
o
f
1
3
,
4
3
5
s
tu
d
en
ts
.
T
h
e
b
r
ea
k
d
o
wn
b
y
f
ac
u
lty
is
d
etailed
in
T
ab
le
3
.
Hav
in
g
estab
lis
h
ed
a
clea
r
u
n
d
e
r
s
tan
d
in
g
o
f
th
e
o
v
er
all
s
tu
d
en
t
d
em
o
g
r
a
p
h
ics
an
d
p
r
ef
er
en
ce
s
,
we
n
o
w
f
o
cu
s
o
n
a
m
o
r
e
d
etailed
ex
am
i
n
atio
n
th
r
o
u
g
h
clu
s
ter
in
g
an
al
y
s
is
.
B
y
ap
p
l
y
in
g
v
ar
io
u
s
clu
s
ter
in
g
alg
o
r
ith
m
s
,
we
aim
to
u
n
co
v
er
d
is
tin
ct
s
tu
d
en
t
p
r
o
f
iles
th
at
ca
n
f
u
r
t
h
er
in
f
o
r
m
s
tr
ateg
ic
p
lan
n
in
g
an
d
r
ec
r
u
itm
en
t e
f
f
o
r
ts
.
Acc
o
r
d
in
g
t
o
T
ab
le
1
,
th
e
Fa
cu
lty
o
f
Hu
m
an
ities
an
d
So
ci
al
Scien
ce
s
h
as
th
e
h
ig
h
est
en
r
o
llm
en
t,
f
o
llo
wed
b
y
th
e
Facu
lty
o
f
M
an
ag
em
en
t
Scien
ce
s
,
th
e
Facu
lty
o
f
Scien
ce
,
T
ec
h
n
o
lo
g
y
,
a
n
d
Ag
r
icu
ltu
r
e,
an
d
th
e
Facu
lty
o
f
E
d
u
ca
tio
n
.
Mo
s
t
s
tu
d
en
ts
o
r
ig
in
ate
f
r
o
m
th
e
s
o
u
th
er
n
r
eg
i
o
n
,
p
a
r
ticu
lar
ly
t
h
e
s
o
u
th
er
n
b
o
r
d
e
r
p
r
o
v
in
ce
s
.
Yala
Pro
v
in
ce
h
as
th
e
h
ig
h
est
n
u
m
b
er
o
f
s
tu
d
e
n
ts
,
f
o
llo
wed
b
y
Pattan
i,
Nar
ath
iwat,
So
n
g
k
h
la,
Satu
n
,
an
d
o
th
e
r
p
r
o
v
in
ce
s
,
as
d
ep
icted
in
T
ab
le
4
.
T
h
e
m
ajo
r
it
y
o
f
s
tu
d
e
n
ts
p
r
ev
io
u
s
ly
atten
d
ed
T
h
am
wittay
am
u
ln
iti
Sch
o
o
l
i
n
Yala
Pro
v
in
ce
,
f
o
llo
wed
b
y
Dar
u
s
s
alam
Sch
o
o
l
in
Nar
at
h
iwat
Pro
v
in
ce
,
an
d
o
th
er
n
o
te
d
in
s
titu
tio
n
s
lis
ted
in
T
ab
le
5
.
C
u
r
r
e
n
tly
,
Yala
R
ajab
h
at
Un
iv
er
s
ity
o
f
f
e
r
s
5
4
d
if
f
er
en
t
p
r
o
g
r
am
s
.
T
h
e
m
o
s
t
p
o
p
u
lar
p
r
o
g
r
am
s
t
u
d
en
ts
ch
o
o
s
e
is
th
e
B
ac
h
elo
r
o
f
Po
liti
ca
l
Scien
ce
,
f
o
llo
we
d
b
y
th
e
B
ac
h
elo
r
o
f
L
aws,
B
ac
h
elo
r
o
f
Acc
o
u
n
tin
g
,
an
d
Ma
n
a
g
em
en
t
p
r
o
g
r
am
,
a
s
d
etailed
in
T
ab
le
6
.
T
ab
le
3
.
Nu
m
b
er
o
f
s
tu
d
en
ts
b
y
f
ac
u
lty
(
2
0
1
9
–
2
0
2
3
)
No
F
a
c
u
l
t
y
N
u
mb
e
r
P
e
r
c
e
n
t
a
g
e
1
F
a
c
u
l
t
y
o
f
H
u
ma
n
i
t
i
e
s
a
n
d
S
o
c
i
a
l
S
c
i
e
n
c
e
s
5
,
2
1
3
3
8
.
8
0
2
F
a
c
u
l
t
y
o
f
M
a
n
a
g
e
me
n
t
S
c
i
e
n
c
e
s
3
,
6
6
9
2
7
.
3
1
3
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
,
Te
c
h
n
o
l
o
g
y
a
n
d
A
g
r
i
c
u
l
t
u
r
e
2
,
3
0
1
1
7
.
1
3
4
F
a
c
u
l
t
y
o
f
E
d
u
c
a
t
i
o
n
2
,
2
5
2
1
6
.
7
6
T
ab
le
4
.
Stu
d
e
n
ts
’
s
ch
o
o
l
p
r
o
v
in
ce
s
(
2
0
1
9
–
2
0
2
3
)
No
P
r
o
v
i
n
c
e
N
u
mb
e
r
P
e
r
c
e
n
t
a
g
e
1
Y
a
l
a
4
,
9
9
6
3
7
.
1
9
2
P
a
t
t
a
n
i
3
,
7
8
5
2
8
.
1
7
3
N
a
r
a
t
h
i
w
a
t
2
,
7
54
2
0
.
50
4
S
o
n
g
k
l
a
6
3
0
4
.
6
9
5
S
t
u
l
5
4
4
4
.
0
5
6
O
t
h
e
r
7
2
6
5
.
4
0
T
ab
le
5
.
T
o
p
1
0
p
r
e
v
io
u
s
ed
u
c
atio
n
al
in
s
titu
tio
n
s
o
f
s
tu
d
en
ts
(
2
0
1
9
–
2
0
2
3
)
No
S
c
h
o
o
l
n
a
m
e
P
r
o
v
i
n
c
e
N
u
mb
e
r
P
e
r
c
e
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T
h
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in
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aly
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s
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o
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s
tu
d
en
t
d
em
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g
r
ap
h
ics
an
d
p
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f
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at
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ity
(
T
a
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s
3
-
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)
.
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ig
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d
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m
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is
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ly
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ter
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o
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ith
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s
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ct
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tu
d
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t
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.
3
.
2
.
Clus
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T
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2
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1
.
Clus
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Dete
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ter
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u
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
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lec
E
n
g
&
C
o
m
p
Sci
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SS
N:
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-
4
7
5
2
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n
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a
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d
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ta
in
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l
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R
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ja
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h
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t
Un
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n
a
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eg
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ter
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d
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ct
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h
e
r
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lts
in
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icate
th
at
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m
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clu
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ter
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g
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ig
h
ly
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ec
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e
f
o
r
t
h
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ataset,
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r
o
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id
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g
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m
ea
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l
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g
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p
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e
f
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g
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ec
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3
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2
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2
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ults
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ajab
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n
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ts
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aw,
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ala
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e
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ter
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with
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with
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ath
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d
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g
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h
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th
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l
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h
at
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in
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I
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1319
T
h
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u
ca
tio
n
al
in
s
titu
tio
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s
ar
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in
v
ar
i
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p
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d
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(
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–
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.
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)
.
T
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f
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am
s
ch
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at
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ala
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s
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ab
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.
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ab
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1
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.
Pr
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o
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s
in
clu
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ter
3
No
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ter
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:
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6
0
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f
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m
v
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tio
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s
titu
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h
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ab
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No
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