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Ag
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R
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Ko
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s
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en
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wsu
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k
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1.
I
NT
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D
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k
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I
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Valley
C
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.
Ag
r
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o
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elate
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(
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-
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)
%
to
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Gr
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Pro
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wh
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a
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if
ican
t
ef
f
ec
t
o
n
th
e
I
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d
ian
ec
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n
o
m
y
.
Ag
r
icu
ltu
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e
p
lay
s
an
im
p
o
r
ta
n
t
p
a
r
t
in
I
n
d
i
a’
s
s
o
cial
an
d
ec
o
n
o
m
ic
s
y
s
tem
an
d
is
th
e
la
r
g
est
ec
o
n
o
m
ic
s
eg
m
e
n
t
in
ter
m
s
o
f
d
em
o
g
r
a
p
h
ics
[
1
]
.
C
r
o
p
o
u
tp
u
t
p
r
ed
ictio
n
ca
n
h
elp
th
e
g
o
v
er
n
m
en
t
b
u
ild
cr
o
p
in
s
u
r
an
ce
p
o
licies
an
d
s
u
p
p
ly
ch
ain
o
p
er
atio
n
p
o
licies
u
s
in
g
b
ig
d
ata
an
aly
s
is
[
2
]
.
I
t
ca
n
al
s
o
h
elp
f
ar
m
er
s
b
y
s
u
p
p
ly
in
g
t
h
em
with
a
p
r
ed
icti
o
n
o
f
th
e
p
ast cr
o
p
y
ield
r
ec
o
r
d
th
at
d
ec
r
ea
s
es r
is
k
m
an
a
g
em
en
t
[
3
]
.
T
h
e
s
u
m
o
f
d
ata
is
r
is
in
g
ex
p
o
n
en
tially
,
wh
ile
th
e
s
p
ee
d
o
f
e
s
tim
atio
n
is
s
lo
win
g
d
o
wn
.
I
n
s
tan
ce
s
o
f
l
ar
g
e
d
ata
in
cl
u
d
e
c
r
o
p
p
r
o
d
u
c
tio
n
,
th
e
f
ield
u
s
ed
,
an
d
cr
o
p
y
ield
.
Sin
ce
t
h
e
g
o
v
er
n
m
en
t
s
y
s
tem
atica
lly
an
d
co
n
tin
u
o
u
s
ly
g
ath
er
s
d
ata
o
n
cr
o
p
p
r
o
d
u
ctio
n
an
d
y
ield
,
th
e
s
ca
le
o
f
th
e
d
ataset
is
k
n
o
wn
to
b
e
b
ig
d
ata,
wh
ich
is
r
ea
l
-
wo
r
ld
d
ata
th
at
is
v
er
y
d
if
f
icu
lt
to
in
ter
p
r
et
[
4
]
.
Statis
tical
m
eth
o
d
s
an
d
d
ata
m
in
in
g
ca
n
b
e
ex
ten
d
ed
u
n
d
er
d
is
tr
ib
u
te
d
an
d
p
ar
allel
co
m
p
u
tin
g
p
latf
o
r
m
s
to
an
aly
ze
b
ig
d
ata
an
d
o
f
ten
co
n
s
u
m
es
h
u
g
e
p
r
o
ce
s
s
in
g
tim
e
an
d
v
o
l
u
m
e
o
f
s
to
r
ag
e
to
ac
co
m
m
o
d
ate
v
ast
d
ata
s
ets
[
5
]
.
Data
m
in
i
n
g
tech
n
i
q
u
e
p
lay
s
a
cr
u
cial
r
o
le
in
d
ata
an
aly
s
is
.
Data
m
in
in
g
is
a
s
u
b
f
ield
o
f
in
ter
d
is
cip
lin
ar
y
co
m
p
u
ter
s
cien
ce
an
d
an
aly
tics
with
an
o
v
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all
tar
g
et
o
f
id
e
n
tify
in
g
tr
e
n
d
s
,
p
atter
n
s
,
an
d
ass
o
ciatio
n
s
with
in
b
r
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ad
d
a
ta
s
ets
th
at
in
clu
d
e
s
tr
ateg
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at
th
e
in
ter
s
ec
t
io
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o
f
m
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lear
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g
,
d
atab
as
e
s
y
s
tem
s
,
an
d
s
tati
s
tic
s
[
6
]
.
D
a
t
a
m
i
n
i
n
g
u
t
il
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z
es
s
p
e
c
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al
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d
s
t
a
ti
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t
i
c
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a
l
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o
r
i
t
h
m
s
w
it
h
t
h
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u
l
t
i
m
at
e
p
u
r
p
o
s
e
o
f
c
o
l
l
e
c
t
i
n
g
d
a
t
a
b
y
s
e
g
m
en
t
i
n
g
t
h
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d
a
t
a
a
n
d
c
o
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v
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r
t
i
n
g
t
h
e
i
n
f
o
r
m
a
t
i
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i
n
to
a
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s
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a
n
d
a
b
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f
r
a
m
e
w
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k
t
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d
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t
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r
m
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t
h
e
p
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b
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l
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y
o
f
f
u
t
u
r
e
e
v
e
n
t
s
[
7
]
.
T
h
er
e
ar
e
two
k
in
d
s
o
f
lear
n
in
g
a
p
p
r
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ac
h
es
to
d
ata
m
i
n
in
g
:
u
n
s
u
p
er
v
is
ed
(
clu
s
ter
in
g
)
an
d
s
u
p
e
r
v
is
ed
(
class
if
icatio
n
s
)
[
8
]
.
C
lu
s
ter
in
g
is
th
e
p
r
ac
tice
o
f
e
v
alu
atin
g
a
lis
t
o
f
“
d
ata
p
o
in
ts
”
an
d
s
o
r
t
in
g
th
em
ac
co
r
d
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
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A
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ta
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lysi
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s
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K
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B
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p
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99
to
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is
tan
ce
ca
lc
u
latio
n
in
to
s
ep
ar
ate
“c
lu
s
ter
s
”
[
9
]
.
W
h
en
g
r
o
u
p
in
g
th
ese
d
ata
p
o
in
ts
,
th
e
g
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al
s
h
o
u
ld
b
e
f
o
r
d
ata
p
o
in
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in
th
e
s
am
e
clu
s
ter
to
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e
a
s
m
all
d
is
tan
ce
f
r
o
m
ea
ch
o
th
er
,
wh
er
ea
s
d
ata
p
o
in
ts
in
s
ep
ar
ate
clu
s
ter
s
s
h
o
u
ld
b
e
lo
n
g
-
d
is
tan
ce
f
r
o
m
ea
ch
o
th
er
[
1
0
]
.
Data
is
g
r
o
u
p
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i
n
to
well
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f
o
r
m
ed
class
es
th
r
o
u
g
h
clu
s
te
r
an
aly
s
is
.
T
h
e
n
o
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m
al
d
ata
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tr
u
ctu
r
e
ca
n
b
e
ca
p
tu
r
e
d
b
y
well
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f
o
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m
ed
clu
s
ter
s
[
1
1
]
.
T
h
is
p
ap
er
aim
s
to
less
en
th
e
m
an
u
al
wo
r
k
o
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ap
p
ly
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alg
o
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ith
m
s
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s
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if
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e
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m
o
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les.
T
h
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ap
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s
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b
ased
lib
r
ar
ies
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d
as,
s
ea
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o
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n
,
K
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s
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p
r
in
cip
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co
m
p
o
n
en
t
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aly
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is
(
PC
A
)
,
to
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ls
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f
u
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an
d
m
eth
o
d
s
to
q
u
ick
ly
an
aly
ze
,
m
in
e,
an
d
v
is
u
alize
th
e
ag
r
icu
ltu
r
e
d
ataset.
T
h
e
d
ataset
is
v
i
s
u
alize
d
u
s
in
g
d
is
tp
lo
t
co
m
b
in
ed
with
a
k
er
n
el
d
en
s
ity
esti
m
ate
(
KDE
)
p
lo
t.
K
-
m
ea
n
s
cl
u
s
ter
in
g
tech
n
iq
u
e
is
u
s
ed
in
th
e
cu
r
r
en
t
w
o
r
k
to
f
o
r
m
clu
s
ter
s
f
r
o
m
th
e
ag
r
icu
ltu
r
al
d
ata
s
et
.
C
o
m
p
ar
ed
to
o
th
er
clu
s
ter
in
g
alg
o
r
ith
m
s
,
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
is
ex
tr
em
ely
s
im
p
le
to
im
p
lem
en
t
an
d
is
also
v
er
y
e
f
f
ec
tiv
e
in
co
m
p
u
ta
tio
n
,
wh
ich
m
a
y
ex
p
lain
its
p
o
p
u
lar
ity
.
T
h
e
clu
s
ter
s
o
b
tain
e
d
ar
e
v
is
u
alize
d
b
y
r
ed
u
cin
g
t
h
eir
d
im
en
s
io
n
s
u
s
in
g
p
r
in
ci
p
al
co
m
p
o
n
en
t
a
n
aly
s
is
.
T
h
e
r
em
ain
d
er
o
f
t
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws:
s
ec
tio
n
2
ex
p
lain
s
th
e
m
eth
o
d
o
l
o
g
y
f
o
r
v
is
u
alizin
g
an
d
clu
s
ter
in
g
th
e
d
ataset.
Sectio
n
3
p
r
esen
ts
th
e
r
esu
lts
an
d
f
in
ally
,
s
ec
tio
n
4
c
o
n
clu
d
es with
s
o
m
e
d
i
r
ec
tio
n
s
f
o
r
f
u
tu
r
e
wo
r
k
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
O
L
O
G
Y
T
h
is
p
ap
er
aim
s
t
o
p
r
o
p
o
s
e
a
m
eth
o
d
to
a
n
aly
ze
ag
r
icu
lt
u
r
al
d
ata
u
s
in
g
d
ata
m
in
in
g
t
ec
h
n
iq
u
es.
Ag
r
icu
ltu
r
e
d
ata
h
as
b
ee
n
o
b
t
ain
ed
f
r
o
m
cr
ed
ib
le
s
o
u
r
ce
s
i
n
th
e
p
r
o
p
o
s
ed
w
o
r
k
.
I
n
p
u
t
d
a
taset
co
n
s
is
t
o
f
d
ata
with
f
o
llo
win
g
p
ar
am
eter
s
n
a
m
ely
:
c
r
o
p
n
am
e
,
p
r
o
d
u
ctio
n
(
2
0
0
6
-
2
0
1
1
)
,
ar
ea
(
2
0
0
6
-
2
0
1
1
)
,
y
ield
(
2
0
0
6
-
2
0
1
1
)
[
1
2
]
.
I
n
th
e
p
r
o
p
o
s
ed
wo
r
k
,
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
m
eth
o
d
is
u
s
ed
to
clu
s
ter
d
ata
b
a
s
ed
o
n
cr
o
p
s
with
id
en
tical
o
u
tp
u
t,
ar
ea
,
an
d
y
ield
am
o
u
n
ts
[
1
3
]
.
Dis
tp
lo
t
co
m
b
in
ed
with
Ker
n
el
d
en
s
ity
e
s
tim
atio
n
(
KDE
)
is
u
s
ed
f
o
r
v
is
u
alizin
g
th
e
p
r
o
b
a
b
ilit
y
d
en
s
ity
at
d
if
f
er
en
t
v
alu
es
in
a
co
n
tin
u
o
u
s
v
ar
iab
le
o
f
th
e
d
ataset
wh
ich
ca
n
im
p
r
o
v
e
its
p
r
ed
ictio
n
ac
cu
r
ac
y
.
T
h
e
p
r
in
cip
al
co
m
p
o
n
en
t
an
aly
s
is
is
u
s
ed
f
o
r
d
im
en
s
io
n
ality
r
ed
u
ctio
n
o
f
th
e
d
ataset
at
k
ee
p
i
n
g
th
e
o
r
ig
in
a
l
in
f
o
r
m
atio
n
u
n
ch
an
g
ed
[
1
4
]
.
T
h
e
o
p
tim
u
m
p
ar
am
eter
s
f
o
r
m
a
x
im
u
m
o
u
tp
u
t c
a
n
b
e
o
b
tain
ed
b
ased
o
n
th
is
an
aly
s
is
.
C
lu
s
ter
in
g
is
th
e
p
r
o
ce
s
s
o
f
d
iv
id
in
g
a
d
ataset
in
to
g
r
o
u
p
s
s
u
ch
th
at
en
titi
e
s
in
ea
ch
c
lu
s
ter
ar
e
co
m
p
ar
ativ
ely
m
o
r
e
s
im
ilar
to
en
titi
es
o
f
th
a
t
clu
s
ter
th
a
n
th
o
s
e
o
f
t
h
e
o
t
h
er
clu
s
ter
s
.
I
n
a
d
ataset,
C
lu
s
ter
in
g
ca
n
r
ev
ea
l
u
n
d
etec
ted
co
n
n
ec
t
io
n
s
.
I
n
th
e
p
r
o
p
o
s
ed
wo
r
k
,
w
e
h
av
e
u
s
ed
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
to
cl
u
s
ter
o
u
r
ag
r
icu
ltu
r
al
d
ata.
T
h
e
K
-
m
ea
n
s
alg
o
r
ith
m
b
elo
n
g
s
to
th
e
p
r
o
to
ty
p
e
-
b
ased
clu
s
ter
i
n
g
g
r
o
u
p
.
Pro
t
o
ty
p
e
-
b
ased
m
eth
o
d
s
s
ee
k
to
d
ef
in
e
th
e
d
ata
s
et
to
b
e
ca
teg
o
r
ized
o
r
c
lu
s
ter
ed
b
y
a
(
u
s
u
ally
s
m
all)
s
et
o
f
p
r
o
to
ty
p
es,
p
ar
ticu
lar
ly
p
o
in
t
p
r
o
t
o
ty
p
es,
wh
ich
ar
e
s
im
p
ly
d
ata
s
p
ac
e
p
o
in
ts
[
1
5
]
.
E
ac
h
p
r
o
t
o
ty
p
e
is
in
ten
d
ed
to
ca
p
tu
r
e
th
e
d
is
tr
ib
u
t
io
n
o
f
a
g
r
o
u
p
o
f
d
ata
p
o
in
ts
b
ased
o
n
a
d
ef
in
itio
n
o
f
s
im
ilar
ity
to
th
e
p
r
o
to
ty
p
e
o
r
clo
s
en
ess
to
it
s
p
o
s
itio
n
th
at
m
ay
b
e
af
f
ec
ted
b
y
th
e
s
ize
an
d
s
h
ap
e
p
ar
am
eter
s
o
f
th
e
(
p
r
o
to
ty
p
e
-
s
p
ec
if
ic)
[
1
6
]
.
Ou
r
g
o
al
is
to
g
r
o
u
p
th
e
d
ataset
b
ased
o
n
th
eir
s
im
ilar
i
ty
in
ch
ar
ac
ter
is
tic
s
,
wh
ich
ca
n
b
e
ac
co
m
p
lis
h
ed
u
s
in
g
th
e
alg
o
r
ith
m
K
-
m
ea
n
s
th
at
ca
n
b
e
s
u
m
m
ar
i
s
ed
in
th
e
f
o
llo
win
g
s
ix
s
tep
s
[
1
7
]
in
Fig
u
r
e
1
.
C
h
o
o
s
e
n
u
m
b
e
r
o
f
c
l
u
s
t
e
r
s
“
K
”
S
e
l
e
c
t
r
a
n
d
o
m
K
p
o
i
n
t
s
t
h
a
t
a
r
e
g
o
i
n
g
t
o
b
e
t
h
e
c
e
n
t
r
o
i
d
s
o
f
e
a
c
h
c
l
u
s
t
e
r
C
a
l
c
u
l
a
t
e
a
n
e
w
c
e
n
t
r
o
i
d
f
o
r
e
a
c
h
c
l
u
s
t
e
r
A
s
s
i
g
n
e
a
c
h
d
a
t
a
p
o
i
n
t
t
o
t
h
e
n
e
a
r
e
s
t
c
e
n
t
r
o
i
d
,
d
o
i
n
g
s
o
w
i
l
l
e
n
a
b
l
e
u
s
c
r
e
a
t
e
“
K
”
n
u
m
b
e
r
o
f
c
l
u
s
t
e
r
s
G
o
t
o
s
t
e
p
4
a
n
d
r
e
p
e
a
t
R
e
a
s
s
i
g
n
e
a
c
h
d
a
t
a
p
o
i
n
t
t
o
t
h
e
n
e
w
c
l
o
s
e
t
c
e
t
r
o
i
d
Fig
u
r
e
1
.
Step
s
f
o
r
ap
p
l
y
in
g
K
-
m
ea
n
s
clu
s
ter
in
g
Me
asu
r
in
g
s
im
ilar
ity
b
etwe
en
o
b
jects:
s
im
ilar
ity
is
d
ef
in
ed
as
th
e
o
p
p
o
s
ite
d
is
tan
ce
,
an
d
th
e
s
q
u
ar
ed
E
u
clid
ea
n
d
is
tan
ce
b
etwe
en
t
wo
p
o
in
ts
p
an
d
q
in
m
-
d
im
en
s
io
n
al
s
p
ac
e
is
a
co
m
m
o
n
l
y
u
s
ed
d
is
tan
ce
f
o
r
clu
s
ter
in
g
s
am
p
les with
co
n
tin
u
o
u
s
f
ea
tu
r
es
[
1
8
]
.
(
,
)
2
−
∑
(
−
)
2
=
1
=
‖
−
‖
2
2
(
1
)
N
o
t
e
t
h
at
t
h
e
i
n
d
e
x
i
i
n
t
h
e
p
r
e
c
e
d
i
n
g
e
q
u
a
t
i
o
n
r
e
f
e
r
s
t
o
t
h
e
i
th
(
f
e
at
u
r
e
c
o
l
u
m
n
)
d
i
m
e
n
s
i
o
n
o
f
s
a
m
p
le
p
o
i
n
t
s
p
a
n
d
q
.
T
h
e
K
-
m
ea
n
s
a
l
g
o
r
i
t
h
m
c
a
n
b
e
d
e
f
i
n
e
d
as
a
s
i
m
p
l
e
o
p
ti
m
i
z
at
i
o
n
p
r
o
b
l
e
m
b
a
s
e
d
o
n
t
h
is
E
u
c
l
i
d
e
a
n
d
i
s
t
a
n
ce
m
e
t
r
i
c
,
a
n
i
t
e
r
a
ti
v
e
a
p
p
r
o
a
c
h
t
o
m
i
n
i
m
i
z
i
n
g
t
h
e
s
u
m
o
f
s
q
u
a
r
e
s
w
i
t
h
i
n
t
h
e
c
l
u
s
t
e
r
(
W
C
S
S)
[
1
9
]
,
wh
ich
is
o
f
ten
also
ca
lled
clu
s
ter
in
er
tia.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
TEL
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
20
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
98
-
1
0
8
100
=
∑
∑
(
,
)
=
1
=
1
‖
(
)
−
(
)
‖
2
2
(
2
)
wh
er
e
(
)
is
th
e
ce
n
tr
o
id
f
o
r
cl
u
s
ter
j,
(
,
)
is
eq
u
al
to
1
if
th
e
s
am
p
le
(
)
is
in
clu
s
ter
j,
o
th
er
wis
e,
it
s
v
alu
e
is
eq
u
al
to
0
.
O
n
e
o
f
th
e
d
is
ad
v
an
tag
es
o
f
th
is
clu
s
ter
in
g
alg
o
r
ith
m
is
th
at
th
e
n
u
m
b
e
r
o
f
c
lu
s
ter
s
,
k
,
a
p
r
io
r
i,
m
u
s
t
b
e
s
p
ec
if
ied
.
Po
o
r
clu
s
ter
in
g
p
er
f
o
r
m
a
n
ce
m
a
y
r
es
u
lt
in
a
n
in
a
p
p
r
o
p
r
iate
o
p
ti
o
n
f
o
r
k
.
Fo
r
a
n
y
u
n
s
u
p
er
v
is
ed
alg
o
r
ith
m
,
th
e
ca
lcu
latio
n
o
f
th
e
o
p
tim
al
n
u
m
b
er
o
f
cl
u
s
ter
s
in
to
wh
ic
h
th
e
d
ata
m
ay
b
e
clu
s
ter
ed
is
a
f
u
n
d
am
e
n
tal
s
tep
.
On
e
o
f
th
e
m
o
s
t
co
m
m
o
n
m
eth
o
d
s
f
o
r
e
v
alu
atin
g
th
is
o
p
tim
u
m
k
v
alu
e
is
th
e
elb
o
w
m
eth
o
d
[
2
0
]
.
Usi
n
g
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
m
eth
o
d
u
s
in
g
th
e
s
k
lear
n
p
y
th
o
n
lib
r
ar
y
,
we
ar
e
n
o
w
d
em
o
n
s
tr
atin
g
t
h
e
p
r
o
v
id
ed
m
eth
o
d
.
2
.
1
.
Cre
a
t
ing
a
nd
v
is
ua
lizin
g
t
he
da
t
a
Data
v
is
u
aliza
tio
n
is
th
e
r
ep
r
e
s
en
tatio
n
o
f
th
e
d
ata
v
alu
es
in
a
p
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r
ial
f
o
r
m
at.
Vis
u
aliza
tio
n
o
f
d
ata
h
elp
s
in
attain
in
g
a
b
etter
u
n
d
er
s
tan
d
in
g
an
d
h
elp
s
d
r
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w
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t
p
er
f
ec
t
co
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clu
s
io
n
s
f
r
o
m
th
e
d
ata.
Data
v
is
u
aliza
tio
n
p
lay
s
a
cr
u
cial
r
o
le
in
an
y
d
ata
an
a
l
y
s
is
[
2
1
]
.
I
t h
elp
s
to
r
ec
o
g
n
ize
wh
ic
h
v
ar
ia
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les
ar
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t
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d
wh
ich
v
ar
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in
f
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en
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r
p
r
e
d
ictio
n
m
o
d
el.
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h
ile
p
r
ep
ar
i
n
g
a
n
y
m
ac
h
in
e
l
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in
g
(
ML
)
m
o
d
el
we
h
av
e
t
o
in
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is
co
v
er
wh
ich
ch
ar
ac
te
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tics
ar
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s
ig
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if
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an
d
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w
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ca
n
af
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ec
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th
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h
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n
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d
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y
an
aly
zin
g
th
e
d
ata
th
r
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u
g
h
d
ata
v
is
u
aliza
tio
n
.
−
P
y
t
h
o
n
s
e
a
b
o
r
n
m
o
d
u
l
e
:
T
h
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d
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P
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M
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s
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a
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n
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d
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d
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t
o
r
i
al
r
e
p
r
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s
e
n
t
a
t
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o
n
[
2
2
]
.
−
D
i
s
t
p
l
o
t
:
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d
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t
p
l
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d
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d
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w
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t
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k
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d
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t
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m
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e
(
K
D
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p
l
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to
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d
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a
v
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es
.
−
K
D
E
p
l
o
t:
I
t is
a
p
l
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t
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at
d
e
p
ic
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s
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p
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f
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d
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.
e
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,
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ca
n
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v
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ab
l
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s
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lt
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g
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t
h
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r
[
2
3
]
.
−
H
e
a
t
m
a
p
s
:
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n
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f
t
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m
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/
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m
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p
s
[
2
4
]
.
D
is
t
p
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b
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t
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p
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(
)
.
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e
h
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v
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r
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2
d
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w
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cl
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4
b
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t
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a
p
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ti
v
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l
y
.
Fig
u
r
e
2
.
Step
s
in
v
o
lv
ed
in
v
is
u
alizin
g
th
e
d
ataset
2
.
2
.
F
ind
ing
nu
m
ber
o
f
clus
t
er
s
K
by
elbo
w
m
et
ho
d
T
h
is
is
p
er
h
ap
s
th
e
b
est
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k
n
o
wn
m
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n
s
o
f
esti
m
atin
g
th
e
o
p
tim
u
m
n
u
m
b
e
r
o
f
clu
s
ter
s
[
2
5
]
.
I
n
its
m
eth
o
d
,
it
is
also
a
b
it
n
aiv
e.
Me
asu
r
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th
e
with
in
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clu
s
ter
s
-
s
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m
o
f
s
q
u
ar
es
(
W
C
SS
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o
r
v
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r
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s
k
v
al
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an
d
p
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e
k
f
o
r
wh
ich
W
C
SS
b
eg
in
s
to
d
im
in
is
h
f
ir
s
t.
T
h
is
is
ev
id
en
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as
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o
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in
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l
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f
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ith
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L
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Fig
u
r
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3
.
W
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to
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eq
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ican
ce
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Scik
it
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will b
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in
Fig
u
r
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4
.
T
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101
f
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3
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a
l c
o
m
po
nent
a
na
ly
s
is
(
P
CA)
I
n
th
e
co
d
e
f
o
r
ap
p
l
y
in
g
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
,
th
e
K
-
m
ea
n
s
o
b
ject
h
as
b
ee
n
c
r
ea
ted
a
n
d
p
ass
ed
as
th
e
n
u
m
b
er
o
f
clu
s
ter
s
“K”
o
b
tain
ed
f
r
o
m
th
e
el
b
o
w
m
eth
o
d
.
I
n
th
e
n
e
x
t
lin
e
f
it
m
et
h
o
d
o
n
K
-
m
ea
n
s
h
as
b
ee
n
ca
lled
an
d
th
e
“c
r
o
p
_
d
f
_
s
ca
led
”
d
ataset
h
as
b
ee
n
p
ass
ed
th
r
o
u
g
h
it
K
-
m
ea
n
s
.
L
a
b
els_
is
u
s
ed
to
s
ee
th
e
lab
els
f
o
r
th
e
d
ata
p
o
in
ts
.
Via
d
im
en
s
io
n
ality
r
ed
u
ctio
n
,
t
h
e
clu
s
te
r
s
we
h
av
e
id
en
tifie
d
af
ter
ap
p
ly
in
g
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
ap
p
r
o
ac
h
ca
n
b
e
v
is
ib
le.
PC
A
is
an
ef
f
ec
tiv
e
to
o
l
f
o
r
v
is
u
alizin
g
h
ig
h
-
d
i
m
en
s
io
n
al
d
ata
in
co
m
b
in
atio
n
with
K
-
m
ea
n
s
.
I
t
is
an
u
n
s
u
p
er
v
is
e
d
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
.
PC
A
p
r
o
jects
th
em
in
to
a
lo
wer
-
d
im
en
s
io
n
al
v
ac
u
u
m
,
r
estricts
th
em
,
an
d
v
is
u
alize
s
th
em
to
o
n
ly
a
f
ew
s
ig
n
if
ica
n
t
k
ey
o
n
es
[
2
6
]
.
Fig
u
r
e
5
d
escr
ib
es
th
e
co
d
e
f
o
r
im
p
lem
en
tin
g
PC
A
o
n
th
e
d
ataset,
ea
ch
b
lo
ck
in
th
is
f
ig
u
r
e
r
ep
r
ese
n
ts
th
e
co
d
e
f
o
r
o
b
tain
i
n
g
th
e
p
r
in
cip
al
co
m
p
o
n
e
n
ts
,
cr
ea
tin
g
a
d
ata
f
r
am
e
with
two
co
m
p
o
n
e
n
ts
,
c
co
n
ca
ten
atin
g
t
h
e
lab
els to
th
e
d
ataf
r
am
e
,
an
d
v
i
s
u
alizin
g
an
d
in
ter
p
r
etin
g
t
h
e
clu
s
ter
s
.
Fig
u
r
e
5
.
Step
s
in
v
o
lv
ed
in
ap
p
ly
in
g
PC
A
o
n
th
e
d
ataset
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
TEL
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
20
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
98
-
1
0
8
102
3.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
I
O
N
3
.
1
.
Vis
ua
lizing
t
he
da
t
a
s
et
T
h
e
d
ataset
m
u
s
t b
e
v
is
u
alize
d
b
ef
o
r
e
a
p
p
ly
in
g
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
t
o
th
e
d
ataset.
R
esu
lts
o
f
d
ata
v
is
u
aliza
tio
n
ar
e
s
h
o
wn
in
Fi
g
u
r
es
6
(
s
ee
Ap
p
en
d
ix
)
an
d
7.
Fig
u
r
e
6
(
s
ee
Ap
p
en
d
ix
)
d
e
p
icts
th
e
KDE
p
lo
t
co
m
b
in
ed
with
d
is
tp
lo
t
is
p
lo
tted
f
o
r
t
h
e
d
ataset
to
an
aly
ze
th
e
d
ata
th
r
o
u
g
h
v
is
u
aliza
tio
n
.
Fig
u
r
e
7
d
ep
icts
th
e
r
esu
lt
o
f
t
h
e
h
ea
tm
ap
p
lo
t
wh
i
ch
is
p
lo
tted
b
y
r
e
p
r
esen
tin
g
t
h
e
d
ataset
in
th
e
f
o
r
m
o
f
a
2
-
d
im
en
s
io
n
al
f
o
r
m
at
f
o
r
f
in
d
in
g
c
o
r
r
elatio
n
s
am
o
n
g
th
e
d
ata.
Fig
u
r
e
7
.
Hea
tm
ap
f
o
r
th
e
g
iv
en
d
ataset
3.
2
.
Clus
t
er
ing
T
o
ca
lcu
late
th
e
K
v
alu
e
(
n
u
m
b
er
o
f
cl
u
s
ter
s
)
,
th
e
elb
o
w
m
eth
o
d
is
ap
p
lied
to
t
h
e
d
a
taset.
T
h
e
o
u
tco
m
e
o
f
t
h
e
elb
o
w
p
r
o
ce
s
s
is
r
ep
r
esen
ted
in
Fig
u
r
e
8
,
an
d
it
d
ep
icts
th
e
r
esu
lt
o
f
t
h
e
elb
o
w
p
lo
t
wh
ich
is
p
lo
tted
u
s
in
g
th
e
with
i
n
-
clu
s
ter
s
u
m
o
f
s
q
u
ar
es
f
o
r
a
r
an
g
e
o
f
v
alu
es
o
f
K
.
T
h
e
o
p
tim
u
m
n
u
m
b
er
o
f
clu
s
ter
s
(
K
v
alu
e)
is
d
eter
m
in
ed
b
y
c
h
o
o
s
in
g
th
e
“e
lb
o
w
”
v
al
u
e
o
f
K,
i.e
.
,
th
e
p
o
in
t
at
wh
ich
th
e
W
C
S
S
s
tar
ts
to
d
ec
r
ea
s
e
lin
ea
r
ly
.
T
h
er
ef
o
r
e,
we
ass
u
m
e
th
at
th
e
n
u
m
b
er
o
f
clu
s
ter
s
i
s
4
f
o
r
th
e
g
iv
en
d
ataset
.
T
ab
le
1
d
ep
icts
th
e
r
esu
l
t
o
f
t
h
e
K
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
.
Fig
u
r
e
9
d
ep
icts
th
e
clu
s
ter
s
we
h
av
e
o
b
tai
n
ed
,
r
e
p
r
esen
ted
b
y
r
ed
u
cin
g
th
eir
d
im
e
n
s
io
n
s
u
s
in
g
Prin
cip
al
co
m
p
o
n
en
t
a
n
aly
s
is
.
C
r
o
p
s
ar
e
co
m
m
o
n
ly
p
ick
ed
f
o
r
th
eir
ec
o
n
o
m
ic
s
ig
n
if
ican
ce
.
T
h
e
ag
r
icu
ltu
r
al
p
la
n
n
in
g
p
r
o
ce
s
s
,
h
o
wev
er
,
in
v
o
lv
es
a
n
esti
m
ate
o
f
th
e
y
ield
o
f
m
an
y
cr
o
p
s
.
I
n
th
is
co
n
tex
t,
u
s
in
g
d
ata
av
ailab
ilit
y
as
th
e
m
ain
m
etr
ic,
5
4
cr
o
p
s
h
a
v
e
b
ee
n
s
elec
ted
f
o
r
t
h
is
wo
r
k
.
C
r
o
p
s
wer
e
o
n
ly
c
h
o
s
en
wh
e
n
ap
p
r
o
p
r
iate
d
ata
s
am
p
les ca
m
e
u
n
d
er
r
ev
iew
in
t
h
e
6
-
y
ea
r
r
a
n
g
e
(
2
0
0
6
-
1
1
)
.
As
a
r
esu
lt
o
f
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
,
4
clu
s
ter
s
ar
e
f
o
r
m
ed
.
C
lu
s
ter
0
r
e
p
r
esen
ts
th
e
cr
o
p
s
h
av
in
g
m
ed
iu
m
p
r
o
d
u
ctio
n
,
h
ig
h
a
r
ea
,
an
d
m
e
d
iu
m
-
lo
w
y
ield
.
C
lu
s
ter
1
r
ep
r
esen
ts
th
e
c
r
o
p
s
h
av
in
g
lo
w
p
r
o
d
u
ctio
n
,
lo
w
ar
ea
,
an
d
m
e
d
iu
m
y
i
eld
.
C
lu
s
ter
2
r
ep
r
esen
ts
th
e
c
r
o
p
s
h
av
in
g
h
ig
h
p
r
o
d
u
ct
io
n
,
m
e
d
iu
m
ar
ea
,
an
d
h
ig
h
y
ield
.
C
lu
s
ter
3
r
ep
r
esen
ts
th
e
cr
o
p
s
h
av
in
g
m
ed
iu
m
-
lo
w
p
r
o
d
u
ctio
n
,
m
e
d
iu
m
-
lo
w
ar
ea
,
an
d
lo
w
y
ield
.
Prin
cip
al
c
o
m
p
o
n
en
t
a
n
aly
s
is
is
u
s
ed
to
r
ep
r
esen
t
th
e
clu
s
ter
s
b
y
r
e
d
u
cin
g
th
ei
r
d
im
en
s
io
n
s
.
T
h
e
p
r
esen
t
wo
r
k
co
v
er
s
th
e
d
i
s
tp
lo
t
co
m
b
in
ed
with
th
e
k
e
r
n
el
d
en
s
ity
esti
m
ate
p
lo
t
an
d
h
ea
tm
ap
f
o
r
v
is
u
aliza
tio
n
.
T
h
e
elb
o
w
m
eth
o
d
is
u
s
ed
f
o
r
f
in
d
in
g
th
e
o
p
tim
al
n
u
m
b
er
o
f
clu
s
ter
s
“K”
.
K
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
is
ap
p
lie
d
to
f
o
r
m
c
lu
s
ter
s
f
r
o
m
th
e
d
ataset.
T
h
e
p
r
in
cip
al
co
m
p
o
n
en
t
an
aly
s
is
is
u
s
ed
to
r
ep
r
esen
t
th
e
clu
s
ter
s
f
o
r
m
ed
b
y
r
ed
u
c
in
g
th
eir
d
im
e
n
s
io
n
s
.
T
h
e
cr
o
p
d
ata
co
llectio
n
ca
n
b
e
a
n
aly
s
ed
u
s
in
g
th
ese
m
eth
o
d
s
an
d
th
e
o
p
tim
u
m
p
a
r
am
eter
s
f
o
r
cr
o
p
p
r
o
d
u
ctio
n
ca
n
b
e
ca
lcu
lated
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
A
g
r
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r
e
d
a
ta
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n
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g
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u
n
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l
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a
d
a
p
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n
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103
Fig
u
r
e
8
.
W
C
SS
v
s
K
p
lo
t (
elb
o
w
m
eth
o
d
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T
ab
le
1
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lu
s
ter
s
o
b
tain
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f
r
o
m
th
e
K
-
m
ea
n
al
g
o
r
ith
m
to
r
e
p
r
esen
t c
r
o
p
s
as p
er
p
r
o
d
u
ctio
n
,
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ea
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n
d
y
ield
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l
u
st
e
r
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r
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p
s
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r
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c
t
i
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r
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e
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r
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