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I
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m
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llectio
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s
o
f
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e
h
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d
i
m
en
s
io
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al
d
ata.
T
h
is
co
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s
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ts
o
f
d
ata
w
i
th
lar
g
e
n
u
m
b
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f
r
ec
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d
s
an
d
a
ttrib
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te
s
.
E
x
t
r
ac
tin
g
m
ea
n
i
n
g
f
u
l
in
f
o
r
m
atio
n
f
r
o
m
r
a
w
d
ata
c
o
u
ld
b
e
a
d
if
f
ic
u
lt
tas
k
.
On
e
w
a
y
to
u
n
d
er
s
ta
n
d
h
i
g
h
d
i
m
en
s
io
n
al
d
ata
is
t
o
d
is
p
la
y
it i
n
a
lo
w
-
d
i
m
en
s
io
n
a
l p
lan
e
[1
].
T
h
e
m
ai
n
m
o
ti
v
atio
n
f
o
r
d
o
m
ain
ex
p
er
ts
in
a
n
al
y
z
in
g
th
eir
m
u
ltid
i
m
e
n
s
io
n
al
d
ata
is
to
d
etec
t
an
d
in
ter
p
r
et
clu
s
ter
s
ep
ar
atio
n
a
n
d
o
u
tlier
s
[2
]
.
P
r
io
r
to
th
at,
w
e
n
ee
d
to
s
t
u
d
y
a
n
d
a
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y
ze
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ig
h
d
i
m
en
s
io
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al
d
ata
to
u
n
d
er
s
tan
d
a
n
d
i
n
ter
p
r
et
t
h
e
r
elatio
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s
h
ip
b
et
w
ee
n
cl
u
s
t
er
an
d
d
ata
attr
ib
u
tes.
Fe
w
(
2
0
0
7
)
s
tated
th
a
t,
h
an
d
li
n
g
g
r
o
w
in
g
h
ig
h
d
i
m
e
n
s
io
n
al
d
ata
ca
u
s
es
d
i
f
f
icu
l
ti
es
esp
ec
iall
y
i
n
clu
s
ter
in
g
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e
m
en
ts
i
n
to
g
r
o
u
p
s
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u
d
in
g
v
is
u
aliza
t
io
n
p
r
o
b
le
m
s
d
u
e
to
d
ata
cl
u
tter
s
o
r
d
is
tr
ac
tin
g
r
es
u
lt
s
.
P
r
o
p
er
cl
u
s
ter
i
n
g
is
a
u
s
e
f
u
l
tech
n
iq
u
e
f
o
r
s
tat
is
tica
l
d
ata
an
al
y
s
is
[4
]
.
I
t
is
a
p
r
o
ce
s
s
o
f
g
r
o
u
p
in
g
d
ata
b
a
s
ed
o
n
t
h
e
s
i
m
ilar
it
y
o
f
t
h
eir
p
r
o
p
er
ties
.
Hig
h
d
i
m
e
n
s
io
n
a
l
d
ata
ca
n
b
e
d
is
p
la
y
ed
in
a
clu
s
ter
ed
r
esu
lt
th
r
o
u
g
h
v
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s
u
al
izatio
n
ap
p
r
o
ac
h
es
in
w
h
ic
h
t
h
er
e
ar
e
m
an
y
tech
n
iq
u
es
t
h
at
ca
n
b
e
u
s
ed
.
On
e
o
f
it
is
th
e
Star
C
o
o
r
d
in
a
te
(
SC
)
tec
h
n
iq
u
e.
SC
tech
n
iq
u
e
is
ab
le
to
r
ev
ea
l
p
atter
n
s
an
d
g
r
o
u
p
s
f
r
o
m
h
i
g
h
d
i
m
e
n
s
io
n
al
d
ata
w
h
ile
s
til
l
s
h
o
w
i
n
g
t
h
e
i
m
p
ac
t
o
f
d
ata
attr
ib
u
tes
in
t
h
e
f
o
r
m
atio
n
o
f
its
p
atter
n
s
an
d
g
r
o
u
p
s
[5
]
.
SC
tech
n
iq
u
e
ca
n
also
r
ev
ea
l
th
e
clu
s
ter
s
th
r
o
u
g
h
m
a
n
ip
u
latio
n
o
f
th
e
ax
es
b
y
tr
ial
-
a
n
d
-
er
r
o
r
.
T
h
u
s
,
th
e
cr
it
ical
q
u
esti
o
n
h
er
e
is
w
h
ic
h
f
ea
t
u
r
e
o
r
d
i
m
en
s
io
n
s
b
est
s
ep
ar
ates
t
h
e
class
e
s
an
d
al
lo
w
clu
s
ter
-
b
ased
d
at
a
class
i
f
icatio
n
.
T
h
e
m
ai
n
p
r
o
b
le
m
is
w
it
h
o
u
t p
r
io
r
k
n
o
w
led
g
e
f
in
d
i
n
g
t
h
e
r
ig
h
t o
n
e
s
is
tr
i
v
ial
[2
]
.
A
d
v
an
ce
s
o
f
h
ig
h
p
er
f
o
r
m
a
n
ce
tech
n
o
lo
g
ies
i
n
t
h
e
f
ield
o
f
m
ed
ici
n
e,
en
g
i
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ee
r
in
g
,
s
ci
en
ce
an
d
b
u
s
i
n
ess
h
a
s
r
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u
lted
i
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t
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e
p
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ctio
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o
f
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g
e
a
m
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n
ts
o
f
d
ata,
w
h
ic
h
i
s
k
n
o
w
n
as
h
ig
h
d
i
m
en
s
io
n
al
d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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C
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p
Sci
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N:
2502
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4752
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g
h
v
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u
aliza
tio
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tech
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e.
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u
a
lizatio
n
i
s
a
tech
n
iq
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e
w
h
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h
tr
an
s
f
o
r
m
s
r
a
w
d
ata
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to
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ap
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f
o
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m
.
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tech
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e
is
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f
th
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f
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ar
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h
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i
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ata.
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h
e
g
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o
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s
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tech
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e
is
,
d
ata
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is
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tio
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d
ata
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im
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attr
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e
r
esu
lts
o
f
v
i
s
u
al
izatio
n
.
Hig
h
d
i
m
e
n
s
io
n
al
d
ata
co
n
s
is
t
o
f
i
n
f
o
r
m
atio
n
in
m
a
n
y
tab
les
t
h
at
ar
e
r
elate
d
to
m
u
ltip
le
d
at
a
r
ec
o
r
d
s
an
d
attr
ib
u
tes.
Fu
r
t
h
er
m
o
r
e,
th
er
e
i
s
a
r
elati
o
n
s
h
ip
b
et
w
ee
n
r
ec
o
r
d
s
an
d
attr
ib
u
tes.
Data
r
ec
o
r
d
s
r
ep
r
e
s
en
t
th
e
d
ata
p
o
in
t
w
h
ile
d
ata
d
i
m
e
n
s
io
n
s
(
at
tr
ib
u
tes)
i
n
v
o
lv
ed
w
i
th
th
e
ax
is
p
o
s
itio
n
i
n
S
C
e
n
v
ir
o
n
m
en
t.
T
h
e
ar
r
an
g
e
m
e
n
t
o
f
d
ata
d
i
m
en
s
io
n
in
SC
is
v
ita
l
s
in
ce
it
a
f
f
ec
ts
t
h
e
ap
p
ea
r
an
c
e
o
f
cl
u
s
ter
i
n
f
u
tu
r
e.
I
n
itiall
y
,
as
a
la
y
m
en
u
s
er
,
th
e
y
w
i
ll
ap
p
ly
a
tr
ial
-
an
d
-
er
r
o
r
m
eth
o
d
to
p
r
o
d
u
ce
v
is
u
a
lizatio
n
.
T
h
e
y
w
ill
ar
r
an
g
e
t
h
e
d
ata
d
im
e
n
s
io
n
r
an
d
o
m
l
y
w
h
ic
h
ar
e
ex
tr
ac
te
d
f
r
o
m
a
d
ata
tab
le.
Ho
w
e
v
er
,
r
an
d
o
m
ar
r
an
g
e
m
e
n
t
d
o
es
n
o
t
r
ev
ea
l
g
o
o
d
clu
s
ter
i
n
g
.
T
h
e
m
ai
n
m
o
t
iv
at
io
n
i
n
ex
p
l
o
r
in
g
cl
u
s
ter
i
n
g
a
n
al
y
s
i
s
is
to
d
eter
m
i
n
e
w
h
eth
er
t
h
e
ar
r
an
g
e
m
e
n
t
o
f
d
ata
d
i
m
en
s
io
n
s
i
n
f
lu
e
n
ce
s
t
h
e
ap
p
ea
r
an
ce
o
f
cl
u
s
ter
in
g
,
an
d
w
h
y
it
is
i
m
p
o
r
ta
n
t
to
k
n
o
w
th
e
co
r
r
ec
t
p
o
s
iti
o
n
s
o
f
d
ata
attr
ib
u
tes
in
ea
ch
a
x
is
.
T
h
er
e
ar
e
t
w
o
r
ea
s
o
n
s
w
h
y
t
h
is
is
i
m
p
o
r
tan
t.
T
h
e
f
ir
s
t
is
t
h
at
u
s
er
s
w
it
h
litt
le
k
n
o
w
led
g
e
o
f
SC
te
ch
n
iq
u
es
m
a
y
lac
k
g
u
id
an
ce
i
n
ex
p
lo
r
in
g
th
e
d
ata,
h
av
i
n
g
t
o
r
eso
r
t
to
tr
ial
-
an
d
-
er
r
o
r
,
an
d
w
o
u
ld
s
u
b
s
eq
u
e
n
t
l
y
f
ee
l
d
is
co
u
r
a
g
ed
Fen
g
et
al.
(
2
0
1
8
)
.
Seco
n
d
,
u
s
er
s
n
ee
d
to
k
n
o
w
th
e
i
m
p
o
r
tan
ce
o
f
at
tr
ib
u
te
p
o
i
n
ts
in
t
h
e
d
ata,
a
n
d
w
h
y
t
h
e
y
a
r
e
ar
r
an
g
ed
t
h
at
w
a
y
.
K
n
o
w
i
n
g
t
h
is
b
ef
o
r
e
h
a
n
d
w
o
u
ld
en
ab
le
th
e
m
to
f
o
r
m
a
g
o
o
d
s
u
m
m
ar
y
an
d
m
a
k
e
a
f
aster
d
ec
is
io
n
.
SC
is
li
m
ited
w
h
e
n
it
co
m
es
to
a
h
ig
h
n
u
m
b
er
o
f
d
ata
d
i
m
en
s
i
o
n
s
an
d
w
i
ll
cl
u
tter
th
e
d
ata
f
o
r
m
atio
n
.
T
h
ir
d
,
it
is
i
m
p
o
r
t
an
t
to
eli
m
i
n
ate
th
e
ir
r
elev
an
t
d
ata
d
i
m
en
s
io
n
s
f
r
o
m
b
ein
g
d
is
p
la
y
ed
[5
]
t
h
at
w
o
u
ld
n
o
t
af
f
ec
t
t
h
e
f
o
r
m
atio
n
o
f
d
ata
a
n
d
a
v
o
id
clu
s
ter
.
Fro
m
t
h
is
,
a
s
et
o
f
q
u
esti
o
n
s
ar
is
e
w
h
ic
h
m
o
ti
v
ate
s
o
u
r
w
o
r
k
:
Ho
w
to
m
o
ti
v
ate
la
y
m
a
n
u
s
er
s
w
it
h
o
u
t
p
r
e
-
k
n
o
w
led
g
e
i
n
S
C
o
n
t
h
e
f
ir
s
t
s
tep
i
n
cl
u
s
ter
i
n
g
a
n
al
y
s
i
s
?
W
h
er
e
is
th
e
co
r
r
ec
t,
p
r
o
p
e
r
p
lace
m
en
t
o
f
d
ata
d
i
m
en
s
io
n
s
?
W
h
y
it
i
s
i
m
p
o
r
tan
t
to
k
n
o
w
t
h
e
r
ig
h
t
p
o
s
iti
o
n
o
f
d
ata
attr
ib
u
te
s
?
W
h
ich
d
ata
d
im
e
n
s
io
n
s
is
ir
r
elev
an
t to
b
e
d
is
p
la
y
i
n
an
S
C
la
y
o
u
t?
I
n
th
e
n
e
x
t p
ar
ag
r
ap
h
,
r
elate
d
w
o
r
k
s
to
th
is
s
t
u
d
y
i
s
r
ev
ie
w
ed
.
P
r
ev
io
u
s
li
ter
atu
r
e
r
elate
d
to
d
i
m
e
n
s
io
n
ar
r
an
g
e
m
en
t
i
n
S
C
w
il
l b
e
d
is
c
u
s
s
ed
i
n
d
etail.
A
s
in
tr
o
d
u
ce
d
b
y
Ka
n
d
o
g
an
,
R
o
ad
,
&
J
o
s
e
(
2
0
0
0
)
,
SC
i
s
a
s
i
m
p
le
a
n
d
e
f
f
i
cien
t
tech
n
iq
u
e
f
o
r
v
i
s
u
a
lizi
n
g
m
u
ltid
i
m
en
s
io
n
al
d
ata.
SC
w
o
r
k
s
b
y
p
r
ese
n
ti
n
g
d
ata
p
o
in
ts
u
s
i
n
g
v
ec
to
r
s
u
m
o
f
attr
ib
u
tes
v
al
u
es
alo
n
g
t
h
e
ax
is
.
I
n
t
h
i
s
p
ap
er
,
th
e
y
p
r
o
v
id
ed
th
e
u
s
er
s
w
i
th
th
e
ab
il
it
y
to
v
ie
w
cl
u
s
ter
s
,
tr
en
d
s
an
d
o
u
tlier
s
i
n
t
h
e
d
is
tr
ib
u
tio
n
o
f
d
ata.
C
lu
s
ter
a
n
al
y
s
i
s
i
s
o
f
ten
o
n
e
o
f
t
h
e
f
ir
s
t
s
tep
s
i
n
th
e
an
a
l
y
s
is
o
f
d
ata.
Ho
w
e
v
er
,
t
h
er
e
ar
e
s
o
m
e
w
ea
k
n
e
s
s
e
s
i
n
ex
p
o
s
in
g
t
h
e
clu
s
ter
s
p
atter
n
,
t
h
e
lar
g
es
t o
f
w
h
ich
i
s
th
e
ar
r
a
n
g
e
m
e
n
t o
f
d
i
m
en
s
io
n
s
.
T
h
er
e
ar
e
s
ev
er
al
s
t
u
d
ies
w
h
i
ch
f
o
cu
s
o
n
d
i
m
e
n
s
io
n
ar
r
an
g
e
m
en
t.
T
h
e
f
ir
s
t
p
ap
er
is
f
o
u
n
d
in
y
ea
r
1
9
9
8
b
y
A
n
k
er
s
t,
B
er
ch
to
ld
,
&
Kei
m
.
T
h
e
y
s
tated
th
at
t
h
e
o
r
d
er
an
d
ar
r
an
g
e
m
e
n
t
o
f
d
i
m
en
s
io
n
s
p
la
y
s
a
s
ig
n
i
f
ica
n
t
r
o
le
in
p
r
esen
tin
g
m
a
n
y
h
ig
h
-
q
u
alit
y
v
is
u
ali
za
tio
n
tech
n
iq
u
es
s
u
c
h
as
p
ar
allel
co
o
r
d
in
ate,
s
ca
tter
p
lo
t
an
d
m
o
r
e.
I
n
th
eir
p
ap
er
,
d
im
e
n
s
io
n
ar
r
an
g
e
m
e
n
t
is
s
u
e
h
as
b
ee
n
s
h
o
w
n
to
b
e
a
n
N
-
P
p
r
o
b
lem
a
n
d
th
e
y
s
u
g
g
ested
u
s
in
g
h
e
u
r
is
tic
alg
o
r
ith
m
to
d
eter
m
i
n
e
t
h
e
s
i
m
ilar
it
y
o
f
ea
ch
d
ata
d
i
m
e
n
s
i
o
n
.
Data
d
im
e
n
s
io
n
w
it
h
s
i
m
ilar
b
eh
a
v
io
r
s
ar
e
p
la
ce
d
n
ex
t
to
ea
ch
o
t
h
er
.
Yan
g
,
P
en
g
,
W
ar
d
,
&
R
u
n
d
en
s
tein
er
(
2
0
0
3
)
p
r
o
p
o
s
ed
an
in
ter
ac
ti
v
e
h
ier
ar
ch
ical
o
r
d
er
in
g
o
f
t
h
e
d
i
m
en
s
io
n
s
b
as
ed
o
n
th
eir
s
i
m
ilar
itie
s
,
th
u
s
i
m
p
r
o
v
in
g
th
e
m
an
a
g
ea
b
ilit
y
o
f
h
i
g
h
-
d
i
m
e
n
s
io
n
al
d
ataset
s
an
d
r
ed
u
ci
n
g
th
e
co
m
p
lex
it
y
o
f
th
e
o
r
d
er
in
g
.
W
ar
d
&
R
u
n
d
en
s
tei
n
er
(
2
0
0
4
)
ap
p
lied
th
e
co
n
ce
p
t
o
f
cl
u
tter
-
b
as
ed
d
i
m
en
s
io
n
o
r
d
er
in
g
i
n
v
ar
io
u
s
v
is
u
aliza
tio
n
tech
n
iq
u
es
to
r
ed
u
ce
t
h
e
v
is
u
al
clu
tter
.
T
h
en
,
S
u
n
,
T
an
g
,
T
an
g
,
&
Xiao
(
2
0
0
8
)
ca
m
e
o
u
t
w
it
h
t
h
eir
id
ea
o
n
d
esig
n
in
g
d
i
m
en
s
io
n
co
n
f
i
g
u
r
atio
n
s
tr
ate
g
y
to
o
p
ti
m
ize
th
e
o
r
d
er
an
d
an
g
le
o
f
th
e
d
i
m
e
n
s
io
n
ax
es.
T
h
e
y
u
s
e
d
ia
m
eter
as
t
h
e
d
i
m
en
s
io
n
a
x
is
i
n
s
tead
o
f
r
ad
i
u
s
.
I
n
2
0
1
0
,
Di
C
ar
o
,
Fria
s
-
Ma
r
ti
n
ez
,
&
Fria
s
-
Ma
r
ti
n
ez
p
r
esen
ted
o
n
u
n
d
er
s
ta
n
d
in
g
th
e
r
elatio
n
b
et
w
ee
n
t
h
e
ar
r
an
g
e
m
en
t
o
f
d
i
m
e
n
s
io
n
s
an
d
th
e
q
u
a
lit
y
o
f
v
is
u
aliza
t
io
n
u
s
i
n
g
t
h
e
R
ad
v
iz
tech
n
iq
u
e.
Gar
cia
et
al.
(
2
0
1
6
)
p
r
o
p
o
s
ed
an
in
ter
ac
tiv
e
S
tar
C
o
o
r
d
in
ate
(
iSt
ar
)
w
h
ic
h
ca
n
h
a
n
d
le
a
lar
g
e
n
u
m
b
er
o
f
d
ata
d
im
e
n
s
io
n
s
.
T
h
e
y
also
s
t
u
d
ied
h
o
w
t
h
e
o
r
d
er
o
f
d
ata
d
i
m
en
s
io
n
ca
n
h
av
e
a
n
i
m
p
ac
t
o
n
r
ev
ea
li
n
g
p
atter
n
an
d
clu
s
ter
in
g
,
en
ab
li
n
g
u
s
er
s
to
u
n
d
er
s
ta
n
d
th
e
m
ea
s
ier
.
W
an
g
et
al.
(
2
0
1
7
)
s
tu
d
ied
ab
o
u
t
d
eter
m
i
n
in
g
w
h
ic
h
d
i
m
e
n
s
io
n
s
ar
e
r
elev
an
t
o
r
ir
r
elev
an
t
to
b
e
d
is
p
la
y
ed
in
t
h
e
S
C
la
y
o
u
t
w
h
ic
h
co
n
tr
ib
u
tes to
cl
u
s
ter
i
n
g
.
T
h
is
p
ap
er
p
r
esen
ts
th
e
s
tu
d
y
o
f
d
im
e
n
s
io
n
ar
r
an
g
e
m
e
n
t
in
SC
en
v
ir
o
n
m
e
n
t
to
r
ev
ea
l
th
e
clu
s
ter
s
u
s
i
n
g
P
ea
r
s
o
n
C
o
r
r
e
la
tio
n
te
ch
n
iq
u
e
w
i
th
b
asic
k
n
o
w
led
g
e
o
f
SC
tec
h
n
iq
u
e.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
w
h
ich
n
o
t
o
n
l
y
i
m
p
r
o
v
es
t
h
e
ef
f
icie
n
c
y
o
f
a
x
es
m
an
ip
u
latio
n
w
it
h
h
i
g
h
er
clu
s
ter
q
u
ali
t
y
,
b
u
t
a
ls
o
en
ab
les
u
s
er
s
to
lear
n
th
e
r
elatio
n
s
b
et
w
ee
n
clu
s
ter
s
an
d
d
ata
attr
ib
u
tes.
Di
m
e
n
s
io
n
s
ar
e
ar
r
an
g
ed
b
ased
o
n
th
e
s
o
r
ted
co
r
r
elatio
n
v
al
u
e
w
it
h
i
n
t
h
e
s
a
m
e
le
n
g
t
h
o
f
a
x
is
an
d
a
n
g
le.
T
h
e
co
r
r
elatio
n
v
alu
e
s
wh
ich
s
h
o
w
s
i
m
ilar
b
eh
av
io
r
ar
e
p
lace
d
n
ex
t
to
ea
ch
o
th
er
an
d
w
o
u
ld
b
en
e
f
it
f
ir
s
t
-
t
i
m
e
u
s
er
s
u
s
in
g
S
C
tech
n
iq
u
e
b
y
s
er
v
i
n
g
a
s
g
u
id
a
n
ce
w
h
e
n
o
b
s
er
v
i
n
g
t
h
e
clu
s
ter
’
s
ap
p
ea
r
an
ce
.
Firs
tl
y
,
t
h
e
d
is
ta
n
ce
o
f
ea
c
h
d
ata
attr
ib
u
tes
ar
e
ca
lc
u
lated
.
T
h
en
th
e
co
r
r
ela
tio
n
s
b
et
w
ee
n
d
ata
attr
ib
u
te
s
w
er
e
d
eter
m
in
ed
.
T
h
e
co
r
r
elatio
n
v
alu
e
s
f
r
o
m
s
m
a
ll
to
lar
g
e
v
alu
e
w
er
e
s
o
r
ted
.
T
h
e
co
r
r
ela
tio
n
v
a
lu
e
s
w
o
u
ld
p
r
o
d
u
ce
n
e
g
ati
v
e
an
d
p
o
s
iti
v
e
v
alu
e
s
.
W
h
en
p
lo
tti
n
g
t
h
e
d
ata
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
1
2
,
No
.
1
,
Octo
b
er
201
8
:
3
4
8
–
355
350
in
S
C
,
t
h
e
n
e
g
ativ
e
co
r
r
elatio
n
v
al
u
es
f
o
r
ea
ch
d
ata
attr
ib
u
tes
w
ill b
e
p
o
s
itio
n
ed
o
n
t
h
e
le
f
t
s
id
e
w
h
ile
p
o
s
iti
v
e
v
alu
e
w
o
u
ld
b
e
p
lace
d
o
n
th
e
r
ig
h
t sid
e.
T
h
e
r
em
ai
n
i
n
g
p
ar
t
o
f
t
h
is
p
ap
er
is
o
r
g
an
ized
in
t
h
e
f
o
ll
o
w
i
n
g
m
a
n
n
er
:
Sectio
n
2
d
escr
ib
es
th
e
m
et
h
o
d
o
lo
g
y
u
s
ed
.
R
e
s
u
lt
an
d
d
is
cu
s
s
io
n
w
i
ll
b
e
ex
p
lai
n
ed
in
s
ec
tio
n
3
.
L
a
s
tl
y
,
t
h
e
co
n
clu
s
io
n
w
i
ll
b
e
d
is
cu
s
s
ed
in
s
ec
tio
n
4
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
t
h
is
s
ec
tio
n
,
all
t
h
e
p
r
o
ce
s
s
es
an
d
ex
p
er
i
m
en
t
s
t
h
at
h
a
v
e
b
ee
n
d
o
n
e
w
i
ll
b
e
d
i
s
cu
s
s
ed
i
n
d
etail.
S
C
la
y
o
u
t
co
n
s
is
ts
o
f
cir
c
u
lar
l
y
a
r
r
an
g
ed
v
ec
to
r
s
v
i
w
it
h
a
co
m
m
o
n
o
r
ig
i
n
,
ea
ch
v
ec
to
r
co
r
r
e
s
p
o
n
d
in
g
to
a
d
ata
attr
ib
u
te.
Data
in
s
ta
n
ce
s
ar
e
m
ap
p
ed
to
th
e
lay
o
u
t
as
a
lin
ea
r
co
m
b
in
at
io
n
o
f
th
e
v
ec
to
r
s
v
i.
T
o
im
p
r
o
v
e
th
e
u
s
er
e
x
p
er
ien
ce
,
S
C
m
et
h
o
d
s
en
ab
le
i
n
ter
ac
tiv
e
f
ea
tu
r
es
th
a
t
allo
w
u
s
er
s
to
r
o
tate
th
e
a
n
g
le
o
f
t
h
e
v
ec
to
r
s
v
i
to
f
in
d
co
n
f
i
g
u
r
atio
n
s
w
h
er
e
p
atter
n
s
a
n
d
g
r
o
u
p
s
ar
e
m
o
r
e
cl
ea
r
l
y
r
ev
ea
led
.
2
.
1
.
Da
t
a
Co
llect
io
n
T
o
test
th
e
m
et
h
o
d
,
an
au
to
m
o
b
ile
d
atase
t
w
ill
b
e
u
s
ed
in
th
e
e
x
p
er
i
m
e
n
t.
T
h
is
d
ataset
is
a
b
en
ch
m
ar
k
d
ata
th
a
t
is
w
id
el
y
u
s
ed
b
y
v
ar
i
o
u
s
r
esear
ch
er
s
an
d
co
n
s
is
t
o
f
3
9
5
au
to
m
o
b
iles
f
r
o
m
t
h
e
1
9
7
0
’
s
u
n
t
il
1
9
8
0
’
s
.
T
h
e
attr
ib
u
tes
m
ea
s
u
r
ed
h
er
e
ar
e
f
u
el
e
f
f
ici
en
c
y
-
m
ile
s
p
er
g
al
lo
n
(
MP
G)
,
n
a
m
e
o
f
th
e
ca
r
s
(
n
a
m
e)
,
o
r
ig
in
o
f
t
h
e
ca
r
(
o
r
ig
in
)
,
y
ea
r
o
f
th
e
ca
r
(
y
ea
r
)
,
ac
ce
ler
atio
n
,
w
eig
h
t,
h
o
r
s
e
p
o
w
er
,
e
n
g
in
e
d
is
p
lace
m
e
n
t (
d
is
p
lace
m
e
n
t)
a
n
d
n
u
m
b
er
o
f
c
y
lin
d
er
s
(
c
y
li
n
d
er
s
)
.
2
.
2
.
P
r
o
ce
s
s
a
nd
pro
ce
du
re
s
T
h
e
f
ig
u
r
e
b
elo
w
s
h
o
w
s
t
h
e
p
r
o
ce
s
s
o
n
ar
r
an
g
in
g
t
h
e
d
ata
d
im
en
s
io
n
i
n
r
ig
h
t p
o
s
itio
n
.
T
h
er
e
ar
e
s
ix
s
tep
s
i
n
th
is
p
r
o
ce
s
s
.
Deta
il
s
ar
e
d
is
cu
s
s
ed
b
e
lo
w
:
Fig
u
r
e
1
.
T
h
e
p
r
o
ce
s
s
o
f
d
i
m
e
n
s
io
n
ar
r
an
g
e
m
e
n
t i
n
SC
e
n
v
ir
o
n
m
e
n
t
Ste
p 1
: Calcu
la
te
d
is
ta
n
ce
.
Me
th
o
d
u
s
ed
in
ca
lc
u
lati
n
g
d
is
ta
n
ce
is
E
u
c
lid
ea
n
d
is
ta
n
ce
.
E
q
.
√
∑
(
)
T
o
d
eter
m
i
n
e
t
h
eir
s
i
m
ilar
it
y
,
a
E
u
clid
ea
n
d
is
ta
n
ce
m
ea
s
u
r
e
is
u
s
ed
.
R
e
s
u
lts
ar
e
u
s
ed
to
d
eter
m
in
e
th
e
s
i
m
ilar
it
y
ar
r
an
g
e
m
en
t o
f
d
i
m
en
s
io
n
s
as i
n
Step
2
.
Ste
p 2
: P
ea
r
s
o
n
co
r
r
elatio
n
,
.
E
q
.
(
∑
)
(
∑
)
(
∑
)
√
(
∑
(
∑
)
)
(
∑
(
∑
)
)
I
n
o
r
d
er
to
d
eter
m
i
n
e
t
h
e
co
r
r
elatio
n
b
et
w
ee
n
ax
is
,
t
h
e
P
ea
r
s
o
n
f
o
r
m
u
la
i
s
u
s
ed
.
T
h
is
s
tep
is
cr
u
c
ial
to
e
n
s
u
r
e
th
e
p
o
s
itio
n
o
f
ea
c
h
ax
is
is
co
r
r
ec
t so
th
at
th
e
ap
p
ea
r
an
ce
o
f
ea
ch
clu
s
ter
ca
n
b
e
id
en
ti
f
ied
.
Ste
p 3
: So
r
t th
e
co
r
r
elatio
n
v
a
lu
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
S
ta
r
C
o
o
r
d
in
a
te
Dimen
s
io
n
A
r
r
a
n
g
eme
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t u
s
in
g
E
u
clid
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a
n
Dis
ta
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a
n
d
(
N
o
o
r
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la
iz
a
A
b
d
u
l Kh
a
lid
)
351
I
n
th
i
s
s
tep
,
ea
ch
ax
is
w
ill
b
e
s
o
r
ted
f
r
o
m
t
h
e
h
ig
h
est
to
t
h
e
lo
w
est
v
al
u
e.
T
h
e
s
o
r
ted
ax
is
ca
n
b
e
r
ef
er
r
ed
as
i
n
t
h
e
T
ab
le
1
.
Ho
w
e
v
er
,
n
o
t
a
ll
a
x
es
w
ill
b
e
s
elec
ted
,
if
th
e
co
r
r
elatio
n
v
alu
e
is
o
u
t
o
f
r
an
g
e
[
-
0
.
5
,
0
.
5
]
.
T
h
e
attr
ib
u
te
o
f
Or
ig
i
n
a
n
d
A
cc
eler
atio
n
ar
e
n
o
t
ch
o
s
e
n
to
b
e
d
is
p
la
y
ed
i
n
t
h
e
v
i
s
u
a
lizatio
n
s
in
ce
it d
o
es
n
’
t
m
ee
t t
h
e
r
an
g
e
.
T
ab
le
1
.
A
n
ex
a
m
p
le
o
f
s
o
r
ted
attr
ib
u
tes
w
it
h
co
r
r
elatio
n
v
al
u
e
u
s
in
g
MP
G
attr
ib
u
te
as a
n
an
ch
o
r
A
n
c
h
o
r
:
M
P
G
C
o
r
r
e
l
a
t
i
o
n
V
a
l
u
e
C
h
o
se
n
A
x
e
s
M
P
G
w
i
t
h
Y
e
a
r
0
.
5
8
0
3
8
4
M
P
G
w
i
t
h
O
r
i
g
i
n
0
.
4
7
9
5
4
9
M
P
G
w
i
t
h
A
c
c
e
l
e
r
a
t
i
o
n
0
.
4
2
0
5
7
4
M
P
G
w
i
t
h
C
y
l
i
n
d
e
r
-
0
.
7
7
7
1
4
M
P
G
w
i
t
h
H
o
r
se
p
o
w
e
r
-
0
.
7
7
8
4
3
M
P
G
w
i
t
h
D
i
s
p
l
a
c
e
me
n
t
-
0
.
8
0
5
2
5
M
P
G
w
i
t
h
W
e
i
g
h
t
-
0
.
8
3
2
2
8
Ste
p 4
: P
lo
t th
e
d
ata
d
im
e
n
s
io
n
(
attr
ib
u
tes)
w
it
h
s
a
m
e
a
n
g
le
b
et
w
ee
n
ea
ch
a
x
is
1.
Neg
ati
v
e
o
n
t
h
e
lef
t.
2.
P
o
s
itiv
e
o
n
th
e
r
i
g
h
t.
As
ca
n
b
e
s
ee
n
,
v
ec
to
r
ad
d
itio
n
s
w
it
h
i
n
t
h
e
S
C
s
p
ac
e
m
u
s
t
b
e
v
alid
,
in
o
r
d
er
to
p
r
o
j
ec
t
all
th
e
d
ata
p
o
in
ts
co
r
r
ec
tl
y
o
n
an
S
C
.
I
t
is
a
clo
ck
w
is
e
a
n
g
le
b
et
w
ee
n
ax
es.
T
h
e
p
o
s
itiv
e
co
r
r
elatio
n
v
alu
e
i
s
p
lace
d
o
n
th
e
r
i
g
h
t
s
id
e,
w
h
i
le
n
eg
at
iv
e
co
r
r
elatio
n
v
a
lu
e
is
p
lace
d
o
n
t
h
e
le
f
t
s
id
e.
T
h
e
o
u
tli
n
e
o
f
ax
i
s
p
o
s
itio
n
ed
i
s
s
h
o
w
n
b
elo
w
i
n
Fi
g
u
r
e
2
.
Fig
u
r
e
2
.
T
h
e
o
u
tlin
e
o
f
ax
is
p
o
s
itio
n
b
as
ed
o
n
o
b
tain
s
co
r
r
elatio
n
v
al
u
e
B
ased
o
n
T
ab
le
1
,
th
e
f
ir
s
t
a
x
is
to
b
e
p
o
s
itio
n
ed
o
n
t
h
e
r
i
g
h
t
s
id
e
a
f
ter
a
n
a
n
ch
o
r
,
MP
G
w
it
h
t
h
e
p
o
s
itiv
e
co
r
r
elatio
n
v
a
lu
e
i
s
Yea
r
,
f
o
llo
w
ed
b
y
n
eg
a
tiv
e
c
o
r
r
elatio
n
v
alu
e
w
h
ic
h
is
C
y
l
in
d
er
,
Ho
r
s
ep
o
w
er
,
Dis
p
lace
m
e
n
t a
n
d
W
eig
h
t.
Ste
p 5
:
I
d
en
tify
cl
u
s
ter
s
Af
ter
p
lo
ttin
g
th
e
a
x
is
,
an
ea
r
l
y
cl
u
s
ter
ap
p
ea
r
an
ce
is
f
o
r
m
e
d
.
T
h
is
clu
s
ter
ca
n
b
e
an
in
itia
l
g
u
id
eli
n
e
to
th
e
la
y
m
e
n
to
d
o
m
o
r
e
d
ata
ex
p
lo
r
atio
n
.
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
B
ef
o
r
e
i
m
p
le
m
en
t
in
g
p
r
o
p
o
s
ed
m
et
h
o
d
,
w
e
ca
m
e
o
u
t
w
i
th
r
an
d
o
m
p
lo
tti
n
g
.
T
h
i
s
is
to
s
h
o
w
w
h
et
h
e
r
p
lo
ttin
g
r
a
n
d
o
m
l
y
ca
n
p
r
o
d
u
ce
clu
s
ter
r
es
u
lts
o
r
n
o
t.
R
e
s
u
l
ts
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
3
.
Fig
u
r
e
3
s
h
o
w
s
r
an
d
o
m
l
y
p
lo
t
ted
d
ata
d
im
e
n
s
io
n
s
.
As
ill
u
s
t
r
ated
f
r
o
m
th
is
f
i
g
u
r
e,
w
e
ar
e
u
n
ab
le
to
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o
th
ex
tr
ac
t
a
n
y
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s
e
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l
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n
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o
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m
atio
n
an
d
d
e
f
i
n
e
t
h
e
r
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n
s
h
i
p
b
et
w
ee
n
d
ata
d
i
m
en
s
io
n
s
.
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h
e
r
es
u
lt
o
n
l
y
s
h
o
w
s
t
h
e
d
is
tr
ib
u
tio
n
o
f
d
ata
th
at
r
ep
r
esen
ts
h
i
g
h
d
i
m
e
n
s
io
n
al
d
ata
v
is
u
aliza
tio
n
in
(
a)
.
I
n
(
b
)
it
s
h
o
w
s
t
h
at
m
p
g
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p
lo
tted
as a
n
an
c
h
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to
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a
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i
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e
t
h
e
co
m
p
r
o
m
is
e
b
et
w
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n
o
t
h
er
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ata
at
tr
ib
u
tes.
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w
e
v
er
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h
e
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o
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itio
n
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o
f
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ata
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ib
u
tes
ar
e
p
lace
d
r
eg
ar
d
less
o
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i
n
ter
r
elate
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n
e
s
s
b
et
w
ee
n
attr
ib
u
te
d
ata.
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y
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in
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e
r
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o
r
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o
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er
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ar
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t
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e,
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h
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ig
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lo
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ted
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o
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o
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ite.
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ased
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b
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ata
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tte
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i
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ial
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et
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n
f
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tu
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h
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t
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r
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et
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n
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ata
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it
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ea
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g
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r
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c)
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ter
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t
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t
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o
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n
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o
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e
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in
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at
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e,
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ata
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lo
tted
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a
n
ch
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ata
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a
s
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d
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ter
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ta
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g
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m
e
n
t,
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u
t
th
r
o
u
g
h
tr
ial
-
a
n
d
-
er
r
o
r
p
r
o
ce
s
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
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2
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I
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l
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1
2
,
No
.
1
,
Octo
b
er
201
8
:
3
4
8
–
355
352
Ste
p 1
:
Ra
nd
o
m
P
lo
t
t
ing
a)
N
A
M
A
b)
M
P
G
c)
H
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EPO
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d)
D
I
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P
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Fig
u
r
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3
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ll
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e
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r
e
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lt.
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h
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ed
m
et
h
o
d
.
Ste
p2
:
Re
m
o
v
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v
a
lue in r
a
ng
e
[
-
0
.
5
,
0
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5
]
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h
is
ex
p
er
i
m
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o
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e
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ata
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u
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ac
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ata
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ata
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elate
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ata
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m
in
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atter
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u
r
e
4
s
h
o
w
s
an
e
x
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m
p
l
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th
at
tak
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MP
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as
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an
ch
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ac
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d
o
r
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n
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ata
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im
en
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u
r
e
4
.
A
cc
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a
n
d
Or
i
g
in
d
ata
d
i
m
en
s
io
n
w
i
ll b
e
r
em
o
v
ed
as it i
s
in
r
a
n
g
e
[
-
0
.
5
,
0
.
5
]
Ste
p 3
:
P
lo
t
t
he
s
elec
t
ed
da
t
a
a
t
t
ribute
s
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h
en
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ata
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u
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h
a
v
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g
ati
v
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alu
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w
i
ll b
e
p
o
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ed
o
n
th
e
le
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t sid
e,
wh
ile
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o
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iti
v
e
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al
u
e
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il
l b
e
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itio
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r
ig
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t
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ac
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ata
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h
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m
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le
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ar
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h
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o
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n
t a
n
d
y
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r
.
T
h
ese
r
esu
lt
s
ar
e
illu
s
tr
ated
in
T
ab
le
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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RE
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[
1
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S
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–
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9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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355
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