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I
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Usi
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ased
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2
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[
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[
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Step
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as f
o
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s
[
1
5
-
16]
:
1
.
T
h
e
o
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j
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ts
P
a
r
titi
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to
k
n
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t e
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p
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y
s
u
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2
.
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d
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3
.
E
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ass
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d
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to
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ter
.
4
.
Sto
p
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m
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ch
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ter
,
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th
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w
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s
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g
o
b
ac
k
to
s
tep
2
.
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h
ile
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th
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e
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ter
o
f
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ch
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s
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d
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m
l
y
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t
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m
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;
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d
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g
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f
u
zz
y
v
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th
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F
ig
u
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3.
W
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=
2
; h
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.
Step
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al
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s
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A
l
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ith
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f
K
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m
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s
cl
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s
ter
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g
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I
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ar
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ase
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clu
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ter
(
K)
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t d
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ase
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
Usi
n
g
th
e
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u
z
z
y
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g
ic
to
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in
d
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tima
l Cen
tr
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f Cl
u
s
ters
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f K
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i)
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eg
in
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h
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th
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ata
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ax
i
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ata
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r
ith
=1
to
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7
0
Step
4
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r
j
w
=1
to
k
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tep
4
-
1
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m
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it
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t j
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t it
h
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ate
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ter
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ter
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id
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m
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tate
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u
m
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er
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th
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s
ta
tes i
n
th
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t c
lu
s
ter
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tep
6
:
I
f
th
e
ce
n
ter
o
f
ea
ch
clu
s
ter
d
o
n
o
t c
h
an
g
e
s
to
p
else g
o
to
s
tep
3.
E
n
d
Fig
u
r
e
3
.
K
-
Me
an
s
C
la
s
s
i
f
ica
t
io
n
f
o
r
Hea
r
t D
is
ea
s
e
4.
CO
NCLU
SI
O
N
K
-
m
ea
n
s
is
a
co
m
m
o
n
cl
u
s
ter
in
g
al
g
o
r
ith
m
t
h
at
n
ee
d
s
a
n
en
o
r
m
o
u
s
i
n
itial
s
et
to
s
tar
t
t
h
e
clu
s
ter
i
n
g
.
E
x
p
er
i
m
e
n
tal
r
esu
lt
s
h
o
w
t
h
at,
k
-
m
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n
s
is
h
u
m
b
le
an
d
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s
y
t
o
u
n
d
er
s
tan
d
b
u
t
th
e
m
ai
n
d
r
aw
b
ac
k
is
r
an
d
o
m
l
y
in
f
ir
s
t
s
tep
(
s
elec
tio
n
in
i
tial
ce
n
ter
s
o
f
clu
s
t
er
s
)
.
In
t
h
i
s
w
o
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k
,
w
e
u
s
ed
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f
u
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y
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t
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.
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h
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e
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ir
s
t
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t
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th
is
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ea
r
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h
w
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p
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y
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ch
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ata
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d
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ase,
w
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h
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el
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ch
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e
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s
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ter
o
f
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ch
clu
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ter
.
T
h
e
ac
cu
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ac
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o
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t
h
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w
o
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k
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s
9
5
.
4
8
%,
w
h
ile
t
h
e
ac
cu
r
ac
y
o
f
t
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e
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m
ea
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s
w
it
h
o
u
t f
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zz
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ic
o
n
t
h
e
s
a
m
e
d
ata
w
as 9
0
.
7
8
%.
RE
F
E
R
E
NC
E
S
[1
]
Am
it
K.
,
“
A
rti
f
i
c
ial
In
telli
g
e
n
c
e
a
n
d
S
o
f
t
Co
m
p
u
ti
n
g
,
”
CRC
P
re
ss
LL
C
,
p
p
.
2
,
2
0
0
0
.
[2
]
G.
K
a
rth
ig
a
,
e
t
a
l
.
,
“
He
a
rt
Dis
e
a
se
A
n
a
l
y
sis
S
y
ste
m
Us
in
g
D
a
t
a
M
in
in
g
T
e
c
h
n
iq
u
e
s
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
n
o
v
a
ti
v
e
Res
e
a
rc
h
i
n
S
c
ien
c
e
,
E
n
g
i
n
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l/
i
ss
u
e
:
3
(3
)
,
2
0
1
4
.
[3
]
Ha
n
J.
a
n
d
Ka
m
e
r
M
,
“
Da
ta M
in
i
n
g
:
Co
n
c
e
p
ts
a
n
d
T
e
c
h
n
i
q
u
e
s
,
”
M
o
rg
a
n
Ka
u
fm
a
n
n
P
u
b
li
sh
e
r
,
2
0
0
1
.
[4
]
N
.
S
.
Ch
a
u
d
h
u
ri
a
n
d
A
.
G
h
o
sh
,
“
F
e
a
tu
re
Ex
trac
ti
o
n
u
sin
g
f
u
z
z
y
ru
le
b
a
se
s
y
ste
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Co
mp
u
ter
S
c
ien
c
e
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l/
issu
e
:
5
(
3
)
,
p
p
.
1
–
8
,
2
0
0
8
.
[5
]
C
.
Zh
a
n
g
a
n
d
Z
.
F
a
n
g
,
“
A
n
Im
p
ro
v
e
d
K
-
m
e
a
n
s
Clu
ste
rin
g
A
lg
o
rit
h
m
,
”
J
o
u
rn
a
l
o
f
In
f
o
rm
a
ti
o
n
&
Co
mp
u
ta
t
io
n
a
l
S
c
ien
c
e
,
v
o
l/
issu
e
:
1
0
(
1
)
,
2
0
1
3
.
[6
]
L
.
M
o
risse
tt
e
a
n
d
S
.
C
h
a
rti
e
r,
“
T
h
e
k
-
m
e
a
n
s
c
lu
ste
rin
g
tec
h
n
iq
u
e
:
Ge
n
e
ra
l
c
o
n
sid
e
ra
ti
o
n
s
a
n
d
im
p
lem
e
n
tatio
n
in
M
a
th
e
m
a
ti
c
a
,
”
T
u
to
ri
a
ls
in
Qu
a
n
t
it
a
ti
v
e
M
e
th
o
d
s fo
r
Psy
c
h
o
l
o
g
y
,
v
o
l/
issu
e
:
9
(1
)
,
p
p
.
15
-
24
,
2
0
1
3
.
[7
]
R
.
De
tran
o
,
“
M
e
d
ica
l
Ce
n
ter,
”
L
o
n
g
Eac
h
a
n
d
Clev
e
l
a
n
d
Cli
n
ic F
o
u
n
d
a
t
io
n
.
[8
]
V
.
S
u
n
d
a
ra
p
a
n
d
ian
a
n
d
E.
P.
E
p
h
z
ib
a
h
,
“
F
ra
m
in
g
F
u
z
z
y
Ru
les
u
sin
g
su
p
p
o
rt
se
ts
f
o
r
Ef
f
e
c
ti
v
e
He
a
rt
Dise
a
s
e
Dia
g
n
o
sis
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
F
u
zz
y
L
o
g
ic S
y
ste
ms
(
IJ
FL
S
)
,
v
o
l/
issu
e
:
2
(
1
)
,
p
p
.
11
-
16
,
2
0
1
2
.
[9
]
O.
O.
Ola
d
ip
u
p
o
,
e
t
a
l.
,
“
A
F
u
z
z
y
A
s
so
c
iatio
n
Ru
le
M
i
n
in
g
Ex
p
e
rt
-
Driv
e
n
(F
A
RM
E
-
D)
a
p
p
ro
a
c
h
to
Kn
o
w
led
g
e
A
c
q
u
isit
io
n
,
”
A
frica
n
J
o
u
rn
a
l
o
f
Co
mp
u
t
in
g
&
ICT
,
v
o
l/
issu
e
:
5
(5
)
,
p
p
.
53
-
60
,
2
0
1
2
.
IS
S
N 2
0
0
6
-
1
7
8
1
.
[1
0
]
W
.
S
il
e
r,
e
t
a
l
.
,
“
F
u
z
z
y
Ex
p
e
rt
S
y
ste
m
s an
d
F
u
z
z
y
Re
a
so
n
in
g
,
”
W
il
ley
In
ter
sc
ien
c
e
,
p
p
.
1
-
4
2
4
,
2
0
0
5
.
[1
1
]
R.
Da
s,
e
t
a
l.
,
“
Eff
e
c
ti
v
e
d
ia
g
n
o
sis
o
f
h
e
a
rt
d
ise
a
se
th
ro
u
g
h
n
e
u
ra
l
n
e
tw
o
rk
s
e
n
se
m
b
les
,”
Exp
e
rt
S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
,
El
se
v
ier
,
v
o
l/
issu
e
:
36
(
2
0
0
9
)
,
p
p
.
7
6
7
5
–
7
6
8
0
,
2
0
0
9
.
[1
2
]
T
iru
c
h
e
n
g
o
d
e
a
n
d
Na
m
a
k
k
a
l,
“
P
r
e
d
ictin
g
th
e
A
n
a
ly
sis
o
f
H
e
a
rt
Dise
a
se
S
y
m
p
to
m
s
Us
in
g
M
e
d
icin
a
l
Da
ta
M
i
n
i
n
g
M
e
th
o
d
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
n
Ad
v
a
n
c
e
d
Co
mp
u
ter
T
h
e
o
ry
a
n
d
E
n
g
i
n
e
e
rin
g
(
IJ
ACT
E)
,
v
o
l/
issu
e
:
2
(2
),
p
p
.
2
3
1
9
–
2
5
2
6
,
2
0
1
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
6
,
No
.
6
,
Dec
em
b
er
201
6
:
3
06
8
–
3
07
2
3072
[1
3
]
P
.
M
.
P
a
tel,
e
t
a
l.
,
“
Im
a
g
e
se
g
m
e
n
tatio
n
u
sin
g
K
-
m
e
a
n
c
lu
ste
rin
g
f
o
r
f
in
d
in
g
t
u
m
o
r
in
m
e
d
ica
l
a
p
p
li
c
a
ti
o
n
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
T
re
n
d
s a
n
d
T
e
c
h
n
o
l
o
g
y
(
IJ
CT
T
)
,
v
o
l/
issu
e
:
4
(5
)
,
p
p
.
1
2
3
9
-
1
2
4
2
,
2
0
1
3
.
[1
4
]
O
.
A
.
A
b
b
a
s,
“
Co
m
p
a
riso
n
b
e
t
w
e
e
n
Da
ta
c
lu
ste
rin
g
A
lg
o
rit
h
m
,
”
T
h
e
I
n
ter
n
a
ti
o
n
a
l
Ara
b
J
o
u
r
n
a
l
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l/
issu
e
:
5
(3
)
,
2
0
0
8
.
[1
5
]
R.
Ha
rik
u
m
a
r,
et
a
l.
,
“
P
e
rf
o
rm
a
n
c
e
A
n
a
l
y
sis
f
o
r
Qu
a
li
t
y
M
e
a
su
re
s
Us
in
g
K
-
m
e
a
n
s
Clu
ste
rin
g
a
n
d
EM
M
o
d
e
ls
in
S
e
g
m
e
n
tatio
n
o
f
M
e
d
ica
l
Im
a
g
e
s
,
”
In
t.
J
.
o
f
S
o
f
t
Co
m
p
u
ti
n
g
a
n
d
En
g
in
e
e
rin
g
,
v
o
l/
issu
e
:
1
(
6
)
,
2
0
1
2
.
[1
6
]
M
o
sle
m
M
.
K.,
et
a
l.
,
“
A
p
p
ly
in
g
Ne
w
M
e
th
o
d
f
o
r
Co
m
p
u
ti
n
g
I
n
it
ial
Ce
n
ters
o
f
K
-
m
e
a
n
s
c
lu
ste
rin
g
w
it
h
C
o
lo
r
Im
a
g
e
S
e
g
m
e
n
tatio
n
,
”
J
.
T
h
i
-
Qa
r
S
c
i
.,
v
o
l/
issu
e
:
3
(
1
)
,
2
0
1
1
.
B
I
O
G
RAP
H
Y
O
F
AUTHO
R
W
e
d
K
a
d
h
i
m
O
leiw
i
:
I
g
o
t
a
d
e
g
re
e
Ba
c
h
e
lo
r
o
f
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
th
e
Un
iv
e
rsit
y
o
f
Ba
b
y
lo
n
\
Co
ll
e
g
e
o
f
S
c
ien
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