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I
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in
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i
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cid
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ce
o
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cr
im
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[
1
]
,
a
s
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n
if
ican
t
is
s
u
e
an
d
a
p
r
o
m
in
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t
r
ea
lity
o
f
th
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cu
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r
en
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s
itu
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[
2
]
.
A
p
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o
b
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th
at
lim
its
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f
r
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d
o
m
o
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cr
im
e
p
r
ev
en
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in
s
titu
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(
NC
PI
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[
3
]
an
d
co
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am
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f
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ev
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Mo
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Ph
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T
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4
7
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2
Min
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crime
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6
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d
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latio
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o
f
a
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n
d
3
.
36
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[
7
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e
p
lace
wh
er
e
cr
im
e
in
cid
en
ce
is
co
n
ce
n
tr
ated
.
T
h
er
e
is
k
n
o
w
led
g
e
f
r
o
m
th
e
c
r
im
e
d
ata
in
th
e
m
u
n
icip
ality
.
A
k
n
o
wled
g
e
th
at
ca
n
b
e
u
s
ed
to
p
r
o
v
id
e
a
b
etter
b
asis
f
o
r
f
o
r
m
u
latin
g
co
u
r
s
es
o
f
ac
tio
n
an
d
to
p
r
e
v
en
t
th
e
c
o
m
m
is
s
io
n
o
f
t
h
e
cr
im
e
in
a
g
iv
e
n
p
lace
in
a
g
iv
e
n
tim
e.
Fu
r
th
er
,
a
m
ass
iv
e
n
u
m
b
e
r
o
f
d
o
cu
m
e
n
ts
o
n
cr
im
e
h
a
v
e
b
ee
n
h
a
n
d
led
b
y
p
o
lice
d
ep
ar
tm
en
ts
wo
r
ld
wid
e
an
d
a
r
e
b
ec
o
m
in
g
ev
en
m
o
r
e
co
m
p
licated
.
Dif
f
icu
lty
in
p
r
o
ce
s
s
in
g
a
lar
g
e
am
o
u
n
t
o
f
d
ata
in
v
o
lv
ed
in
cr
im
e
is
o
n
e
o
b
s
ta
cle
f
ac
ed
b
y
in
tellig
en
ce
an
d
l
aw
en
f
o
r
ce
m
en
t
ag
e
n
cies
[
8
]
a
n
d
co
u
ld
ad
v
er
s
ely
af
f
ec
t th
e
ef
f
ec
tiv
e
n
ess
an
d
ef
f
icien
cy
o
f
p
o
lice
p
er
f
o
r
m
a
n
ce
.
B
esi
d
es,
th
er
e
is
a
n
ee
d
to
an
aly
ze
th
e
in
cr
ea
s
in
g
n
u
m
b
er
o
f
cr
im
es
[
9
]
,
s
o
th
at
a
g
en
cies
will
k
n
o
w
th
e
m
o
s
t a
p
p
r
o
p
r
iate
tech
n
iq
u
e
to
ap
p
r
eh
en
d
th
e
cr
im
in
al
an
d
b
ec
o
m
e
m
o
r
e
ad
v
an
ta
g
eo
u
s
o
v
er
th
em
.
Su
ch
an
im
p
o
r
tan
t
d
o
m
ai
n
h
as
b
ee
n
ac
ce
p
tab
le
am
o
n
g
m
an
y
e
x
p
er
ts
an
d
s
p
ec
ialis
ts
in
cr
im
in
al
ju
s
tice
an
d
law
en
f
o
r
ce
m
e
n
t
[
10
]
wh
o
m
u
s
t
b
e
g
u
id
ed
with
em
p
ir
ical
d
at
a
th
at
co
u
ld
b
e
o
f
h
elp
in
p
o
licy
m
ak
in
g
an
d
law
en
f
o
r
ce
m
e
n
t
ac
tio
n
s
[
1
1
]
a
n
d
to
h
av
e
p
r
ac
tical
m
ea
s
u
r
es
an
d
h
elp
p
o
lice
o
f
f
icer
s
an
d
in
v
es
tig
ato
r
s
to
en
h
an
ce
th
eir
cr
im
e
s
o
lu
tio
n
e
f
f
icien
cy
.
Fu
r
th
er
m
o
r
e
,
d
ata
m
in
in
g
was
u
s
ed
to
an
aly
ze
th
e
p
atter
n
f
r
o
m
a
h
u
g
e
am
o
u
n
t
o
f
cr
im
e
r
ec
o
r
d
s
o
r
k
n
o
wled
g
e
d
is
co
v
e
r
y
in
d
atab
ases
(
KDD)
to
g
ain
s
o
m
e
in
f
o
r
m
atio
n
.
I
t
is
th
e
ex
ac
t
f
ield
a
p
p
licab
le
to
a
h
ig
h
-
v
o
lu
m
e
cr
im
e
d
atas
et
th
at
ca
n
d
is
co
v
e
r
h
i
d
d
en
k
n
o
wled
g
e
,
u
n
e
x
p
ec
ted
p
atter
n
s
,
an
d
n
e
w
r
u
les
f
r
o
m
lar
g
e
am
o
u
n
ts
o
f
cr
im
e
d
ata.
L
ik
e
wis
e,
it
is
o
n
e
o
f
th
e
im
p
o
r
t
an
t
ap
p
licatio
n
s
with
m
an
y
t
ask
s
th
at
p
er
f
o
r
m
class
if
icatio
n
,
ass
o
ciatio
n
,
clu
s
ter
in
g
an
d
ea
c
h
o
f
th
em
h
as
i
ts
s
ig
n
if
ican
ce
[
12
]
,
an
d
th
e
k
n
o
wled
g
e
o
b
tain
ed
f
r
o
m
d
ata
m
in
in
g
will su
r
ely
h
elp
p
o
lice
o
f
f
icer
s
in
th
eir
c
o
m
p
lex
task
s
.
I
n
th
e
d
ata
m
in
in
g
f
ield
,
class
if
icatio
n
is
a
co
m
m
o
n
ly
u
s
ed
tech
n
iq
u
e
[
1
3
]
.
I
t
is
an
u
n
a
v
o
i
d
ab
le
task
b
y
wh
ich
th
e
d
ata
co
u
l
d
b
e
class
if
ied
b
y
th
e
p
r
ev
io
u
s
ly
r
ec
o
g
n
ized
class
lab
els
[
14
]
.
C
lass
if
icat
io
n
is
a
s
u
p
er
v
is
ed
ac
tiv
ity
o
f
m
ac
h
i
n
e
lear
n
in
g
t
h
at
g
en
e
r
ates
a
m
o
d
el
b
ased
o
n
lab
eled
d
ata
[
15
]
.
T
h
e
m
o
d
el
is
u
s
ed
f
o
r
class
d
eter
m
in
atio
n
a
n
d
t
h
er
e
ar
e
s
ev
er
al
k
i
n
d
s
o
f
alg
o
r
ith
m
s
f
o
r
class
if
ic
atio
n
,
s
u
ch
as
n
aïv
e
B
ay
es
,
SVM
,
k
-
NN
,
n
eu
r
al
n
etwo
r
k
,
l
o
g
is
tic
r
eg
r
ess
io
n
,
d
ec
is
io
n
tr
e
e
,
r
an
d
o
m
f
o
r
es [
16
].
T
h
e
r
ap
i
d
m
in
e
r
au
t
o
m
o
d
el
i
s
u
s
ed
in
th
e
s
tu
d
y
an
d
s
u
g
g
e
s
ts
th
e
b
est
lear
n
in
g
tech
n
iq
u
e
wh
ich
is
th
e
n
aïv
e
B
ay
es
class
if
ier
.
I
t
i
s
a
p
r
ed
ictiv
e
c
lass
if
ier
th
at
c
an
b
e
u
s
ed
t
o
m
a
k
e
a
d
ec
is
io
n
b
ased
o
n
t
h
e
d
ata
.
As
a
tech
n
iq
u
e
u
s
ed
to
id
e
n
tify
cr
im
e
p
atter
n
s
an
d
im
p
r
o
v
e
th
e
ef
f
icien
cy
o
f
c
r
im
e
s
o
lu
ti
o
n
s
in
th
e
r
an
g
e
o
f
law
en
f
o
r
ce
m
en
t
[1
7
]
,
with
cr
im
e
ca
teg
o
r
ies
s
u
ch
as
b
ar
an
g
ay
wh
er
e
cr
im
es
ar
e
co
m
m
itte
d
,
d
a
y
an
d
m
o
n
th
,
h
o
ts
p
o
ts
,
h
o
t
p
lace
,
s
ea
s
o
n
al,
o
r
f
r
eq
u
e
n
cy
.
T
h
e
cr
im
e
p
at
ter
n
s
to
b
e
e
x
tr
ac
ted
will
b
e
th
e
b
asis
f
o
r
law
en
f
o
r
ce
m
e
n
t a
g
en
cies to
d
ev
el
o
p
cr
im
e
p
r
ev
e
n
tio
n
p
r
o
g
r
am
s
co
m
p
r
e
h
en
s
iv
ely
.
T
h
u
s
,
th
e
p
r
im
ar
y
g
o
a
l
o
f
th
e
s
tu
d
y
is
to
id
en
tify
th
e
p
atter
n
o
f
co
m
m
itted
cr
im
e
in
cid
e
n
ts
th
at
ar
e
th
e
m
o
s
t
p
r
ev
alen
t
in
th
e
m
u
n
icip
ality
b
ased
o
n
th
e
r
ev
is
ed
p
en
al
co
d
e
o
f
th
e
Ph
ilip
p
i
n
es.
Sp
ec
if
ically
,
to
d
eter
m
in
e
t
h
e
m
o
n
th
,
th
e
tim
e,
th
e
d
ay
,
an
d
th
e
b
a
r
an
g
a
y
o
f
San
ch
e
z
Mir
a
s
ig
n
if
ican
tly
o
cc
u
r
r
ed
th
e
m
o
s
t
p
r
ev
alen
t
cr
im
e
.
T
o
b
e
ab
le
to
o
b
tain
t
h
e
cr
im
e
tr
e
n
d
s
,
a
n
aïv
e
B
ay
es
m
o
d
el
wh
ic
h
is
a
class
if
icatio
n
alg
o
r
ith
m
u
s
in
g
th
e
R
ap
id
m
i
n
er
au
to
m
o
d
el
is
u
s
ed
to
an
aly
ze
th
e
s
et
o
f
cr
im
e
d
ata
[
1
8
]
.
Fu
r
th
er
,
th
e
r
esu
lt
b
asis
i
n
p
r
o
p
o
s
in
g
a
n
in
ter
v
en
tio
n
in
a
n
o
n
lin
e
s
y
s
tem
o
r
an
AI
en
v
ir
o
n
m
e
n
t,
a
n
id
ea
l
c
r
im
e
an
aly
s
is
to
o
l
to
b
e
ab
le
to
id
en
tify
cr
im
e
p
att
er
n
s
q
u
ic
k
ly
a
n
d
in
a
n
e
f
f
icie
n
t
m
an
n
er
f
o
r
f
u
tu
r
e
cr
im
e
p
a
tter
n
d
etec
tio
n
an
d
ac
tio
n
,
wh
ich
is
h
o
p
ed
to
b
e
b
en
ef
icial
f
o
r
t
h
e
m
u
n
ici
p
ality
o
f
s
an
ch
ez
m
ir
a
.
-
C
o
n
ce
p
tu
al
f
r
am
ewo
r
k
T
h
e
f
r
am
ewo
r
k
o
f
th
e
s
tu
d
y
was
b
ased
o
n
th
e
k
n
o
wled
g
e
d
is
co
v
er
y
p
r
o
ce
s
s
(
KDP)
il
lu
s
tr
ated
b
y
[
1
9
]
.
T
h
e
KDP
f
ig
u
r
e
was
m
o
d
if
ied
to
s
u
it
th
e
o
b
jectiv
es
o
f
th
e
s
tu
d
y
.
T
h
e
m
o
d
if
ied
v
e
r
s
io
n
wa
s
p
r
esen
ted
in
Fig
u
r
e
1
f
o
llo
win
g
th
e
s
tep
s
f
r
o
m
p
r
ep
r
o
ce
s
s
in
g
wh
er
ein
n
o
is
y
an
d
ir
r
elev
an
t
d
ata
w
er
e
r
em
o
v
e
d
,
s
elec
tio
n
an
d
tr
an
s
f
o
r
m
atio
n
wh
er
e
d
ata
r
ele
v
an
t
to
t
h
e
an
aly
s
is
task
wer
e
r
etr
iev
ed
f
r
o
m
th
e
d
atab
ase
an
d
f
u
r
th
er
tr
a
n
s
f
o
r
m
e
d
o
r
c
o
n
s
o
lid
ated
in
to
f
o
r
m
s
ap
p
r
o
p
r
iate
f
o
r
m
in
in
g
,
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ata
m
in
in
g
wh
er
e
n
aï
v
e
B
ay
es
class
if
ier
was
ap
p
lied
to
ex
tr
ac
t
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ata
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atter
n
s
,
in
t
er
p
r
etatio
n
,
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ev
alu
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wh
er
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ly
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ter
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g
p
atter
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ep
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ased
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tifie
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d
k
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p
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tatio
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wh
er
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v
is
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aliza
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k
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p
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esen
tatio
n
tech
n
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u
es
wer
e
u
s
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to
p
r
esen
t
th
e
m
in
ed
k
n
o
wled
g
e
to
th
e
u
s
er
.
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2
1086
Fig
u
r
e
1
.
T
h
e
s
tep
s
o
f
ex
tr
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g
k
n
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e
f
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o
m
d
ata
2.
RE
S
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ARCH
M
E
T
H
O
D
2
.
1
.
D
a
t
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s
et
T
h
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ataset
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in
th
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co
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f
r
o
m
th
e
r
esu
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ein
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m
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it
cr
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p
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o
d
ic
r
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o
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t
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y
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o
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Ph
ilip
p
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e
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al
p
o
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f
f
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-
s
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ch
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ir
a
f
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m
th
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r
2
0
1
3
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20
19
.
Data
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llected
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ter
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h
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Mic
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Ap
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in
g
o
r
attr
ib
u
tes
n
ee
d
ed
to
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e
t
r
an
s
f
o
r
m
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o
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i
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al
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ata
.
Stan
d
ar
d
izin
g
th
e
d
ata
in
v
o
l
v
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th
e
f
o
llo
win
g
s
t
ep
s
:
1
)
r
em
o
v
in
g
ex
tr
a
s
p
ac
es,
2
)
f
illi
n
g
all
b
lan
k
ce
lls
with
‘
0
’
,
3
)
co
n
v
er
tin
g
n
u
m
b
er
s
s
to
r
ed
as
tex
t
in
t
o
n
u
m
b
er
s
,
4
)
r
em
o
v
in
g
d
u
p
licate
v
alu
es
f
r
o
m
th
e
d
ata
s
et,
5
)
ch
an
g
in
g
tex
t
t
o
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wer
/u
p
p
er
/
p
r
o
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e
r
ca
s
e
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o
r
c
o
n
s
is
ten
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d
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h
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k
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ellin
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.
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h
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ar
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ized
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et
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tr
an
s
f
o
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m
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y
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av
in
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d
a
ta
in
to
C
SV
(
co
m
m
a
s
ep
ar
at
ed
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alu
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f
o
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m
at.
Af
te
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d
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ter
e
d
in
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r
ap
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in
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r
ap
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licatio
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r
p
r
e
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s
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g
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h
e
r
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lt
ca
n
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e
s
ee
n
in
T
ab
le
1
,
it
s
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o
ws
th
at
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et
ar
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g
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ac
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d
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cr
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class
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icatio
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tim
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d
a
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T
ab
le
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.
Sam
p
le
UC
PER d
ata
No
M
o
n
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h
C
r
i
me
T
y
p
e
C
l
a
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f
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c
a
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a
r
a
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g
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y
Ti
me
D
a
y
1
Jan
u
a
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y
I
n
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e
x
C
r
i
me
a
g
a
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s
t
p
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D
a
c
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l
2
3
.
2
4
W
e
d
n
e
s
d
a
y
2
F
e
b
r
u
a
r
y
N
o
n
-
I
n
d
e
x
C
r
i
me
a
g
a
i
n
s
t
p
r
o
p
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t
y
M
a
s
i
si
t
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:
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0
p
m
Th
u
r
sd
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y
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M
a
r
c
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d
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x
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r
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me
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g
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p
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y
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t
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o
0
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N
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me
Tu
e
s
d
a
y
4
M
a
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h
I
n
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C
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me
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g
a
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n
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t
p
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p
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o
p
l
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e
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8
:
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a
t
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d
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y
5
A
p
r
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l
N
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x
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r
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me
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g
a
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S
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A
n
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1
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0
Tu
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s
d
a
y
6
A
p
r
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l
I
n
d
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x
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me
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g
a
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S
a
n
A
n
d
r
e
s
2
0
:
5
0
Th
u
r
sd
a
y
2
.
2
.
N
a
ïv
e
B
a
y
es c
la
s
s
if
ier
I
n
th
is
a
n
aly
s
is
,
th
e
n
aïv
e
B
ay
es
class
if
icatio
n
m
eth
o
d
wa
s
ce
r
tain
to
ex
a
m
in
e
t
h
e
d
ataset
m
in
ed
f
r
o
m
th
e
UC
PER
(
u
n
it
cr
im
e
p
er
io
d
ic
r
ep
o
r
t)
f
o
r
ec
ast p
o
p
u
latio
n
d
ata
r
esu
lt.
T
h
e
class
if
icatio
n
d
is
cu
s
s
es
d
ata
f
r
o
m
v
ar
io
u
s
class
es
in
o
n
e
o
f
th
e
two
p
ar
ts
o
f
s
u
p
er
v
is
ed
lear
n
in
g
.
T
h
e
tr
ai
n
in
g
d
ataset
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ain
s
th
e
m
o
d
el
to
p
r
ed
ict
th
e
u
n
s
p
ec
if
ied
p
o
p
u
l
atio
n
[
20
]
.
E
n
tire
ly
th
ese
alg
o
r
ith
m
s
h
av
e
t
h
eir
s
ty
le
o
f
im
p
lem
en
tatio
n
a
n
d
d
if
f
er
en
t m
et
h
o
d
s
o
f
class
if
icatio
n
[
2
1
]
.
Naiv
e
b
ay
esian
class
if
ier
is
a
s
im
p
le
p
r
o
b
a
b
ilis
tic
clas
s
if
ier
th
at
wo
r
k
s
b
y
a
p
p
ly
in
g
t
h
e
B
ay
es’
th
eo
r
em
alo
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g
with
Naiv
e
ass
u
m
p
tio
n
s
ab
o
u
t f
ea
tu
r
e
in
d
ep
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d
en
ce
.
Desp
ite
its
in
d
ep
en
d
e
n
c
e
ass
u
m
p
tio
n
,
th
e
Naiv
e
B
ay
esian
class
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ier
is
p
r
o
v
ed
to
b
e
q
u
ite
u
s
ef
u
l
in
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o
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elin
g
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c
o
n
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itio
n
s
o
f
th
e
co
m
p
lex
r
ea
l
-
wo
r
l
d
p
r
o
b
lem
[
1
4
]
f
o
r
th
e
th
eo
r
em
b
ased
o
n
th
e
B
ay
es'
s
th
eo
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em
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at
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ets
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e
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r
o
b
ab
ilit
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th
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ased
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ab
o
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n
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itio
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elate
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h
e
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n
t.
B
ay
es’
t
h
eo
r
em
is
s
t
ated
m
ath
em
atica
lly
as
[
2
2
]
.
A
(
B
/
C
)=
A
(
C
/
B
)
A
(
B
)
/
A
(
C
)
-
-
-
-
1
,
w
h
er
e
B
an
d
C
ar
e
th
e
ev
e
n
ts
an
d
A
(
C
)
0
,
A
(
B
/
C
)
–
th
e
lik
elih
o
o
d
o
f
ev
en
t
B
o
cc
u
r
r
in
g
g
iv
en
th
at
C
is
tr
u
e
, A
(
C
/
B
)
-
th
e
lik
el
ih
o
o
d
o
f
e
v
en
t
C
o
cc
u
r
r
in
g
g
iv
en
th
at
B
is
tr
u
e
,
A
(
B
)
an
d
A
(
C
)
ar
e
p
r
o
b
ab
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f
o
b
s
er
v
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g
B
an
d
C
in
d
ep
en
d
en
tly
o
f
ea
c
h
o
th
e
r
.
2
.
3
.
N
a
iv
e
B
a
y
es in r
a
pid
min
er
’
s
a
uto
m
o
del
As
[
2
3
]
u
s
ed
a
l
o
n
g
p
r
o
ce
d
u
r
e
o
r
th
e
b
lack
b
o
x
es
th
at
s
tar
t
with
th
e
b
lan
k
p
r
o
ce
s
s
,
cr
ea
te
p
r
o
ce
s
s
m
o
d
el
b
y
p
u
ttin
g
d
if
f
er
en
t
o
p
er
ato
r
s
lik
e
r
etr
iev
e,
n
u
m
er
i
ca
l
to
p
o
ly
n
o
m
ial,
s
et
r
o
le,
v
alid
atio
n
r
u
le,
an
d
n
aïv
e
B
ay
es
class
if
ier
.
to
p
lo
t
th
e
cr
im
e
d
ata
an
d
to
s
ee
a
d
if
f
er
en
t
s
tatis
tical
ch
ar
t
o
f
cr
im
e
in
d
if
f
er
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t
s
tates
j
u
s
t
to
estab
li
s
h
th
at
h
o
w
ef
f
i
cien
tly
n
aïv
e
B
ay
es
alg
o
r
ith
m
.
L
ik
ewise
[
2
4
]
u
s
ed
a
m
o
d
el
b
ased
o
n
th
e
n
aiv
e
B
ay
es
class
if
ier
b
u
t
en
co
u
r
ag
es
f
u
r
th
e
r
s
tu
d
ies
o
n
th
e
cr
im
in
al
p
r
ed
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n
p
r
o
b
l
em
with
its
n
ew
m
eth
o
d
o
l
o
g
ies f
o
r
b
o
th
c
r
im
e
d
ataset
g
en
er
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n
a
n
d
d
ec
is
io
n
-
m
ak
in
g
with
h
ig
h
er
ac
cu
r
ac
y
r
ates.
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
Min
in
g
th
e
crime
d
a
ta
u
s
in
g
n
a
ïve
B
a
ye
s
mo
d
el
(
Lo
u
r
d
es M.
P
a
d
ir
a
yo
n
)
1087
Au
to
m
o
d
el
is
an
ex
ten
s
io
n
to
r
ap
id
m
in
er
'
s
au
to
m
o
d
el
ex
ten
s
io
n
th
at
s
p
ee
d
s
u
p
th
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p
r
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ce
s
s
o
f
b
u
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in
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an
d
v
alid
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m
o
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ak
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ier
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y
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b
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o
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lack
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o
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es
ar
e
u
s
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.
Au
to
m
o
d
el
ad
d
r
ess
es
th
r
ee
lar
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e
class
es
o
f
p
r
o
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s
:
p
r
ed
ictio
n
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clu
s
ter
in
g
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o
u
tlier
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W
ith
in
th
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p
r
ed
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teg
o
r
y
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ca
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o
lv
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b
o
t
h
class
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d
r
eg
r
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n
p
r
o
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lem
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.
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ca
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ar
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m
p
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au
to
m
o
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el
g
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es
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lis
t
o
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m
o
d
els
th
at
ar
e
s
u
itab
le
f
o
r
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p
r
o
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lem
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el
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e
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lin
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el,
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o
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(
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m
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ata,
p
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o
f
th
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o
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el
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est f
o
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th
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ata
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ataset.
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ay
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ig
h
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m
o
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lim
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llectio
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f
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n
th
is
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tu
d
y
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e
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e
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iv
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teg
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cr
im
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"in
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n
o
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with
th
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f
in
d
in
g
s
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ased
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n
a
m
ix
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r
e
o
f
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cid
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ts
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t
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en
o
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g
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p
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f
ev
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n
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tr
ib
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r
f
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tu
r
es
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f
ea
ch
o
b
s
er
v
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in
m
ac
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h
e
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th
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o
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ith
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m
ak
es
an
ass
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m
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tio
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at
th
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alg
o
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ith
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tr
ea
ts
all
o
f
th
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attr
ib
u
tes
eq
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ally
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ass
u
m
in
g
th
at
attr
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ar
e
all
s
im
i
lar
ly
im
p
o
r
tan
t
an
d
s
tatis
tical
ly
in
d
ep
en
d
e
n
t.
T
h
is
im
p
lies
th
e
wo
r
th
o
f
o
n
e
attr
ib
u
te
h
as
n
o
b
ea
r
in
g
o
n
th
e
wo
r
th
o
f
a
n
o
th
er
.
T
h
e
n
aiv
e
B
ay
es
alg
o
r
ith
m
co
u
n
ts
th
e
n
u
m
b
e
r
o
f
tim
es
ea
ch
co
m
b
in
atio
n
o
f
an
attr
ib
u
te
v
alu
e
with
ea
ch
o
f
th
e
p
o
s
s
ib
le
class
es
o
cc
u
r
s
,
th
en
co
n
v
er
ts
th
e
co
u
n
ts
in
to
p
r
o
b
ab
ilit
ies.
Simp
ly
d
iv
id
e
ea
ch
c
o
u
n
t
b
y
th
e
n
u
m
b
er
o
f
o
b
s
er
v
atio
n
s
in
ea
ch
class
to
g
et
th
e
r
ig
h
t p
r
o
b
ab
ilit
ies.
T
o
b
eg
in
th
e
d
ata
an
aly
s
is
in
r
ap
id
m
in
er
au
t
o
m
o
d
el,
p
r
ep
ar
e
tar
g
et
cr
im
e
d
ataset
in
co
m
m
a
-
s
ep
ar
ated
v
alu
es
(
C
SV
)
f
o
r
m
at
with
d
if
f
e
r
en
t
p
ar
am
eter
s
li
k
e
a
m
o
n
th
,
c
r
im
e
ty
p
e
,
class
if
icatio
n
,
b
a
r
an
g
a
y
,
tim
e,
an
d
d
ay
f
or
th
e
y
ea
r
2
0
1
3
to
20
19
en
ter
e
d
in
to
th
e
r
a
p
id
m
in
er
ap
p
licatio
n
f
r
o
m
th
e
s
o
u
r
ce
h
ar
d
d
r
iv
e.
B
y
s
elec
tin
g
th
e
d
ata
lo
ca
tio
n
,
s
p
ec
if
y
d
ata
f
o
r
m
at,
f
o
r
m
at
c
o
lu
m
n
s
,
an
d
ch
o
o
s
e
wh
er
e
to
s
to
r
e
th
e
d
ata
.
T
h
e
n
aïv
e
B
ay
es
m
o
d
el
is
g
iv
en
b
y
r
a
p
id
m
in
er
a
u
to
m
o
d
el
to
m
ap
th
e
cr
im
e
d
ata
to
u
n
d
e
r
s
tan
d
th
e
v
ar
io
u
s
cr
im
e
s
tatis
tical
ch
ar
ts
.
R
ap
id
m
in
er
o
f
f
er
s
v
ar
io
u
s
ty
p
es
o
f
p
r
o
ce
s
s
in
g
s
ce
n
ar
io
s
lik
e
s
im
ilar
ch
u
r
n
mod
elin
g
[
25
]
,
d
em
a
n
d
b
ased
an
aly
s
is
,
an
d
o
u
tlier
d
etec
tio
n
.
B
u
t
in
th
is
s
tu
d
y
,
t
h
e
r
esea
r
ch
er
s
u
s
e
th
e
r
a
p
id
m
in
er
a
u
to
m
o
d
el
lik
e,
in
Fig
u
r
e
2
.
I
n
r
ap
id
m
in
e
r
,
t
h
e
e
n
te
r
ed
d
ata
ex
is
ts
u
n
d
er
th
e
'
lo
ca
l
r
ep
o
s
ito
r
y
'
o
p
tio
n
.
Fo
r
d
ata
p
l
o
ttin
g
,
a
n
au
to
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o
d
el
m
et
h
o
d
is
p
r
o
v
id
ed
b
y
th
e
r
ap
id
m
in
er
.
Me
t
h
o
d
s
s
u
ch
as
lo
ad
d
ata,
s
elec
t
task
s
,
s
elec
t
in
p
u
ts
m
o
d
el
ty
p
es,
an
d
r
esu
lts
.
Am
o
n
g
th
e
m
o
d
els
g
iv
en
b
y
th
e
au
to
m
o
d
el,
o
n
to
p
o
f
it
was
th
e
n
aïv
e
B
ay
es o
p
er
ato
r
th
u
s
,
th
e
b
est m
o
d
el
f
o
r
th
e
p
r
o
b
lem
w
as th
e
n
aïv
e
B
ay
e
s
m
o
d
el.
Fig
u
r
e
2
.
R
ap
id
m
in
er
au
t
o
m
o
d
el
in
ter
f
ac
e
T
h
e
g
o
al
o
f
th
e
an
aly
s
is
is
to
s
u
p
p
ly
th
e
in
p
u
t
to
th
e
n
aï
v
e
B
ay
es
clas
s
if
ier
alo
n
g
with
th
e
v
alid
atio
n
r
u
le
[
15
]
an
d
s
ee
th
e
r
esu
lts
in
Fig
u
r
e
3
.
T
h
e
p
er
f
o
r
m
a
n
ce
ass
es
s
m
en
t
o
f
th
e
n
aïv
e
B
ay
e
s
class
if
ier
will
b
e
g
iv
en
b
y
th
is
s
etu
p
.
T
o
s
et
u
p
th
e
p
r
o
ce
d
u
r
e
a
n
d
o
b
tain
th
e
d
esire
d
r
esu
lt
u
s
in
g
n
aiv
e
B
ay
es
class
if
ier
,
th
e
f
o
llo
win
g
s
tep
s
ar
e
to
b
e
im
p
l
em
en
ted
in
r
a
p
id
m
in
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
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o
m
p
Sci,
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l.
23
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2
,
Au
g
u
s
t
20
21
:
1
0
8
4
-
1
0
9
2
1088
Fig
u
r
e
3
.
Dis
p
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o
f
n
aïv
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B
ay
es c
lass
if
ier
3.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
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O
N
Fin
ally
,
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ata
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aly
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p
e
r
f
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m
ed
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o
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in
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o
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t
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at
test
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e
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lt’s
attr
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u
tes
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er
v
ed
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asis
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g
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e
v
ar
io
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s
s
co
p
e
s
o
f
t
h
e
cr
im
e
r
ec
o
r
d
s
.
F
ig
u
r
e
4
s
h
o
ws
th
e
cr
im
e
ty
p
es
co
m
m
itted
in
th
e
m
u
n
icip
ality
o
f
S
an
c
h
ez
m
ir
a,
ca
g
ay
an
,
f
o
r
th
e
ca
len
d
ar
y
ea
r
2
0
1
3
-
2
0
1
9
.
I
t
ca
n
b
e
g
lea
n
e
d
in
th
e
f
ig
u
r
e
th
at
f
o
r
in
d
ex
c
r
im
es,
cr
im
es
ag
ai
n
s
t
p
er
s
o
n
s
,
an
d
cr
im
es
a
g
ain
s
t
p
r
o
p
er
ty
g
o
t
th
e
h
ig
h
est
f
o
l
lo
wed
b
y
v
io
latio
n
s
o
f
s
p
ec
ial
p
en
al
laws.
On
th
e
o
th
er
h
a
n
d
,
f
o
r
n
o
n
-
i
n
d
ex
cr
im
es,
th
e
cr
im
es
ag
ain
s
t
c
h
asti
ty
,
cr
im
es
ag
ain
s
t
p
er
s
o
n
al
lib
er
ty
an
d
s
ec
u
r
ity
,
q
u
asi
o
f
f
e
n
s
es
r
ec
o
r
d
ed
t
h
e
h
ig
h
est.
M
o
s
t
o
f
th
e
cr
im
es
co
m
m
itted
v
io
lated
s
p
ec
ial
p
en
al
laws.
I
t
is
f
o
llo
wed
b
y
cr
im
es
ag
ai
n
s
t
th
e
p
er
s
o
n
.
T
h
e
lo
west
n
u
m
b
er
o
f
cr
im
es
wer
e
p
lo
tte
d
o
n
cr
im
es
ag
ain
s
t
pu
b
lic
o
r
d
er
.
Fig
u
r
e
4
.
C
r
im
e
ty
p
e
co
m
m
itt
ed
(
in
d
e
x
an
d
n
o
n
-
in
d
e
x
)
I
t
ca
n
also
g
lan
ce
d
f
r
o
m
th
e
Fig
u
r
e
5
th
at
f
o
r
in
d
e
x
cr
im
e
s
,
b
ar
an
g
ay
ce
n
tr
o
2
h
as
th
e
h
ig
h
est
ca
s
es
f
o
llo
wed
b
y
b
ar
a
n
g
ay
ce
n
tr
o
1
.
B
ar
an
g
a
y
t
o
k
ito
k
an
d
b
ar
an
g
ay
n
ag
r
a
n
g
tay
an
m
a
k
in
g
th
e
m
th
e
lo
west
r
ec
o
r
d
o
f
in
d
ex
cr
im
es.
B
ar
an
g
a
y
ce
n
tr
o
1
h
as
th
e
h
ig
h
est
n
o
n
-
in
d
e
x
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im
es
co
m
m
itted
f
o
llo
wed
b
y
b
ar
an
g
ay
ce
n
tr
o
2
,
b
ar
a
n
g
ay
la
n
g
ag
a
n
,
b
a
r
an
g
ay
ca
llu
n
g
an
,
a
n
d
n
a
m
u
ac
.
B
ar
an
g
ay
to
k
ito
k
p
l
o
tted
th
e
lo
west
n
o
n
-
in
d
ex
cr
im
e
.
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
Min
in
g
th
e
crime
d
a
ta
u
s
in
g
n
a
ïve
B
a
ye
s
mo
d
el
(
Lo
u
r
d
es M.
P
a
d
ir
a
yo
n
)
1089
T
h
e
d
ata
a
ls
o
s
h
o
w
t
h
at
th
e
m
o
s
t
n
o
n
-
i
n
d
ex
cr
im
es
[
4
]
co
m
m
itted
wer
e
s
p
ec
ial
law
s
p
e
cif
ically
R
A
9
2
6
2
o
th
e
r
wis
e
k
n
o
wn
as
v
i
o
len
ce
a
g
ain
s
t
wo
m
en
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eir
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en
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it
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o
llo
we
d
b
y
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io
l
atio
n
s
o
f
R
A
9
1
6
5
co
m
p
r
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e
n
s
iv
e
d
an
g
er
o
u
s
d
r
u
g
s
ac
t.
T
h
e
lo
west
wer
e
ca
s
es
o
f
ac
ts
o
f
lasciv
io
u
s
n
ess
.
Fo
r
in
d
ex
cr
im
e,
it
is
alar
m
in
g
th
at
th
e
cr
im
e
o
f
r
ap
e
r
ec
o
r
d
e
d
th
e
h
ig
h
est
ca
s
es.
I
t
is
f
o
llo
wed
b
y
r
o
b
b
e
r
y
an
d
th
ef
t
wh
ile
ca
r
n
ap
p
in
g
r
e
g
is
ter
ed
th
e
lo
w
est.
Fig
u
r
e
5
.
B
ar
an
g
ay
T
h
e
d
ata
was
also
an
al
y
ze
d
ac
co
r
d
in
g
to
th
e
m
o
n
th
s
wh
en
t
h
e
cr
im
es
wer
e
c
o
m
m
itted
an
d
it
y
ield
ed
th
at
m
o
n
th
o
f
Ma
y
h
as
t
h
e
g
r
ea
test
n
u
m
b
er
o
f
c
r
im
es
co
m
m
itted
in
th
e
m
u
n
icip
ality
.
C
o
m
p
ar
in
g
th
e
d
ata
o
n
in
d
ex
a
n
d
n
o
n
-
in
d
e
x
wh
en
an
aly
ze
d
b
y
m
o
n
th
s
h
o
ws
th
at
Ma
y
h
as
th
e
h
ig
h
est
b
o
th
f
o
r
i
n
d
ex
a
n
d
n
o
n
-
in
d
e
x
cr
im
es
wh
ile
J
an
u
ar
y
an
d
Feb
r
u
ar
y
ar
e
t
h
e
s
af
est
m
o
n
t
h
s
f
o
r
th
eir
lo
w
r
ec
o
r
d
o
f
b
o
th
i
n
d
ex
a
n
d
n
o
n
-
in
d
ex
cr
im
es
[
5
]
.
An
aly
zin
g
Fig
u
r
e
6
s
u
g
g
ests
th
at
f
o
r
in
d
ex
c
r
im
es,
th
e
m
o
n
th
s
o
f
Octo
b
er
an
d
No
v
em
b
e
r
p
l
o
tted
th
e
h
ig
h
est
n
u
m
b
e
r
o
f
cr
im
es
co
m
m
itted
w
h
ile
J
u
n
e
a
n
d
D
ec
em
b
er
h
av
e
th
e
lo
west
in
d
e
x
cr
im
es.
L
o
o
k
in
g
f
u
r
th
er
o
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th
e
f
ig
u
r
e
s
h
o
ws
th
at
Ma
r
ch
h
as
th
e
h
i
g
h
est
n
u
m
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er
o
f
n
o
n
-
in
d
ex
cr
im
es
i
n
th
e
m
u
n
icip
ality
f
o
llo
wed
b
y
J
u
n
e
an
d
Dec
em
b
er
r
esp
ec
tiv
ely
.
Fig
u
r
e
6
.
C
o
m
p
a
r
is
o
n
o
f
in
d
e
x
an
d
n
o
n
-
in
d
ex
b
y
m
o
n
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s
An
aly
zin
g
Fig
u
r
e
7
an
d
i
t
w
as
f
o
u
n
d
o
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t
th
at
cr
im
e
u
s
u
a
lly
o
cc
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r
s
at
2
p
.
m
.
,
f
o
llo
we
d
b
y
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o
s
e
co
m
m
itted
at
1
0
’
clo
ck
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th
e
af
ter
n
o
o
n
wh
ile
n
o
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in
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ex
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m
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e
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at
1
1
a
.
m
.
a
n
d
7
:3
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th
e
e
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en
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g
.
On
th
e
o
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e
r
h
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d
Fig
u
r
e
8
w
h
ich
in
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e
x
cr
im
es
ar
e
c
o
m
m
o
n
ly
co
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[1
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J.
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.
[2
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V.
J.
M
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A.
Ch
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R.
N.
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4
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[3
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NA
TL
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[4
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P
.
P
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d
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.
Talin
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d
An
a
ly
sis Us
in
g
D
a
ta M
in
in
g
Tec
h
n
i
q
u
e
,
”
I
n
ter
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Ad
v
a
n
c
e
d
T
re
n
d
s
in
C
o
mp
u
ter
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c
ien
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e
a
n
d
En
g
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n
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rin
g
,
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l.
8
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o
.
3
,
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0
1
9
,
d
o
i:
1
0
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3
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3
4
/i
jatc
se
/2
0
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9
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5
2
8
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2
0
1
9
.
[5
]
A.
J.
P
.
De
li
m
a
,
“
Ap
p
l
y
i
n
g
Da
ta
M
in
i
n
g
Tec
h
n
iq
u
e
s
i
n
P
re
d
icti
n
g
In
d
e
x
a
n
d
n
o
n
-
I
n
d
e
x
Crime
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
M
a
c
h
i
n
e
L
e
a
rn
in
g
a
n
d
Co
mp
u
ti
n
g
,
v
o
l.
9
,
n
o
.
4
,
2
0
1
9
,
d
o
i:
1
0
.
1
8
1
7
8
/i
jmlc
.
2
0
1
9
.
9
.
4
.
8
3
7
[6
]
R.
S
.
H.
Ba
c
u
li
n
a
o
a
n
d
R.
Ce
b
a
l
lo
s
,
“
An
a
n
a
ly
sis
o
n
th
e
Lo
c
a
ti
o
n
a
n
d
T
y
p
e
o
f
I
n
d
e
x
Crime
s
in
t
h
e
P
h
il
i
p
p
i
n
e
s,”
14
th
N
a
ti
o
n
a
l
C
o
n
v
e
n
ti
o
n
o
n
S
t
a
ti
stics
,
2
0
1
9
.
[7
]
M
.
J.
S
a
n
c
h
e
z
,
“
Crime
ra
te
in
t
h
e
Ca
g
a
y
a
n
Va
ll
e
y
Re
g
io
n
o
f
t
h
e
P
h
il
i
p
p
i
n
e
s
fr
o
m
2
0
0
9
to
2
0
1
4
,
”
S
tatista,
2
0
1
9
.
[On
li
n
e
].
A
v
a
il
a
b
le:
h
tt
p
s://
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sta
.
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m
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ip
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-
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-
c
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/
[8
]
A.
Ja
in
a
a
n
d
V.
B
h
a
tn
a
g
a
r,
“
Cri
m
e
Da
ta
An
a
ly
sis
Us
in
g
P
i
g
wit
h
Ha
d
o
o
p
,
”
C
o
n
fer
e
n
c
e
o
n
I
n
fo
rm
a
ti
o
n
S
e
c
u
rity
&
Priv
,
v
o
l.
1
,
2
0
1
9
.
[9
]
G
.
Du
d
field
,
C.
An
g
e
l,
L.
W.
S
h
e
rm
a
n
,
a
n
d
S
.
To
rre
n
c
e
,
“
Th
e
‘P
o
we
r
Cu
rv
e
’
o
f
Vic
ti
m
Ha
rm
:
Targ
e
ti
n
g
t
h
e
Distrib
u
ti
o
n
o
f
Crime
Ha
rm
In
d
e
x
Va
lu
e
s
Ac
ro
ss
All
Vic
ti
m
s
a
n
d
Re
p
e
a
t
Vic
ti
m
s
o
v
e
r
1
Ye
a
r,
”
Ca
mb
rid
g
e
J
.
Evid
e
n
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e
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Ba
se
d
P
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.
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8
7
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.
[1
0
]
F
.
He
rre
ra
,
R.
S
o
sa
,
a
n
d
T
.
De
lg
a
d
o
,
“
G
e
o
BI
a
n
d
Bi
g
VG
I
fo
r
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m
e
An
a
ly
sis
a
n
d
Re
p
o
rt,
”
2
0
1
5
3
rd
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Fu
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re
In
ter
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e
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,
2
0
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5
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2
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5
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1
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2
.
[1
1
]
I.
S
.
M
a
k
k
i
a
n
d
F
.
Alq
u
ra
sh
i
“
An
Ad
a
p
ti
v
e
M
o
d
e
l
f
o
r
Kn
o
wl
e
d
g
e
M
in
in
g
i
n
Da
tab
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se
s
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O_
M
I
NE
fo
r
Twe
e
ts
Emo
ti
o
n
s
Clas
sifica
ti
o
n
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
d
T
re
n
d
s
in
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ter
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E
n
g
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g
,
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,
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p
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0
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3
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4
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jatc
se
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0
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8
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7
3
2
0
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8
.
[1
2
]
P
.
S
h
a
rm
a
,
D.
S
in
g
h
,
a
n
d
A.
S
in
g
h
,
“
Cl
a
ss
ifi
c
a
ti
o
n
a
l
g
o
ri
th
m
s
o
n
a
larg
e
c
o
n
ti
n
u
o
u
s
ra
n
d
o
m
d
a
tas
e
t
u
sin
g
ra
p
i
d
m
in
e
r
to
o
l,
”
2
0
1
5
2
n
d
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
El
e
c
tro
n
ics
a
n
d
Co
mm
u
n
ica
t
io
n
S
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ste
ms
(
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)
,
2
0
1
5
,
p
p
.
7
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4
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9
,
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o
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9
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CS
.
2
0
1
5
.
7
1
2
5
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0
3
.
[1
3
]
A.
A.
H.
Ole
iwi
a
n
d
A.
O.
Ad
e
b
a
y
o
,
“
Da
ta
M
in
in
g
Ap
p
l
ica
ti
o
n
Us
in
g
Clu
ste
ri
n
g
Tec
h
n
iq
u
e
s
(K
-
M
e
a
n
s
Alg
o
rit
h
m
)
In
th
e
An
a
l
y
sis
o
f
S
t
u
d
e
n
t'
s
Re
su
lt
,
”
J
o
u
r
n
a
l
o
f
M
u
lt
i
d
isc
ip
li
n
a
ry
En
g
in
e
e
rin
g
S
c
ien
c
e
S
tu
d
ies
,
v
o
l
.
5
,
n
o
.
5
,
p
p
.
2
5
8
7
-
2
5
9
3
,
2
0
1
9
.
[1
4
]
R.
Kia
n
i,
S
.
M
a
h
d
a
v
i,
a
n
d
A.
Ke
sh
a
v
a
rz
i,
“
An
a
ly
sis
a
n
d
P
re
d
ictio
n
o
f
Crime
s
b
y
C
lu
ste
rin
g
a
n
d
C
las
sifica
ti
o
n
,
”
In
t
.
J
.
o
f
A
d
v
a
n
c
e
d
Res
e
a
rc
h
i
n
A
rtif
icia
l
I
n
telli
g
e
n
c
e
,
v
o
l.
4
,
n
o.
8
,
2
0
1
5
,
d
o
i
:
1
0
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1
4
5
6
9
/IJARA
I.
2
0
1
5
.
0
4
0
8
0
2
.
[1
5
]
V.
Na
ste
sk
i,
“
An
o
v
e
rv
iew
o
f
th
e
su
p
e
rv
ise
d
m
a
c
h
i
n
e
lea
rn
in
g
m
e
th
o
d
s,”
Ho
rizo
n
s
,
p
p
.
5
1
-
6
2
,
2
0
1
7
,
doi
:
1
0
.
2
0
5
4
4
/HORIZON
S
.
B
.
0
4
.
1
.
1
7
.
P
0
5
.
[1
6
]
M
.
Aru
m
a
ise
lv
a
m
a
n
d
R.
An
it
a
jes
i,
“
S
tu
d
y
o
f
Cl
u
ste
rin
g
M
e
t
h
o
d
s
in
Da
ta
M
i
n
in
g
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
D
a
t
a
M
in
i
n
g
T
e
c
h
n
i
q
u
e
s
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l
.
7
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n
o
.
0
1
,
p
p
.
5
5
-
5
9
,
2
0
1
8
.
[1
7
]
P
.
Da
s,
A.
K.
Da
s,
J.
Na
y
a
k
,
D.
P
e
lu
si,
a
n
d
W
.
Din
g
,
“
In
c
re
m
e
n
tal
c
las
sifier
in
c
rime
p
re
d
ictio
n
u
sin
g
b
i
-
o
b
jec
ti
v
e
P
a
rti
c
le S
wa
rm
Op
ti
m
iza
ti
o
n
,
”
I
n
fo
rm
a
ti
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n
S
c
ien
c
e
s
,
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l.
5
6
2
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7
9
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0
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0
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1
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o
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1
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6
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.
in
s.
2
0
2
1
.
0
2
.
0
0
2
.
[1
8
]
M.
P
.
G
a
tp
a
n
d
a
n
a
n
d
S
.
C.
Am
b
a
t,
“
M
i
n
in
g
c
rime
i
n
sta
n
c
e
re
c
o
rd
s
o
f
P
h
il
ip
p
in
e
Na
ti
o
n
a
l
P
o
li
c
e
District
Vi
P
ro
v
i
n
c
e
o
f
Ca
v
it
e
,
P
h
il
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p
i
n
e
s:
An
E
x
p
lo
ra
to
r
y
S
tu
d
y
t
o
E
n
h
a
n
c
e
Crime
P
re
v
e
n
ti
o
n
P
ro
g
ra
m
s,”
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
i
n
S
o
c
i
a
l
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d
Hu
m
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n
it
ies
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2
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o
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3
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2
6
5
0
0
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H
-
02
-
20
17
-
0
3
0
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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20
21
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1092
[1
9
]
A.
Ba
n
sa
l,
M
.
S
h
a
rm
a
,
a
n
d
S
.
G
o
e
l,
“
Im
p
ro
v
e
d
K
-
m
e
a
n
Clu
st
e
rin
g
Al
g
o
rit
h
m
fo
r
P
re
d
icti
o
n
An
a
ly
sis
u
si
n
g
Clas
sifica
ti
o
n
Tec
h
n
i
q
u
e
i
n
Da
t
a
M
in
in
g
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Co
mp
u
ter
Ap
p
li
c
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ti
o
n
s
,
v
o
l.
1
5
7
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n
o
.
6
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p
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5
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ij
c
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2
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7
9
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1
9
.
[2
0
]
M
.
Vu
ra
l
a
n
d
M
.
G
ö
k
,
“
Crimin
a
l
p
re
d
ictio
n
u
sin
g
Na
iv
e
Ba
y
e
s
t
h
e
o
ry
,
”
Ne
u
ra
l
Co
m
p
u
t
&
A
p
p
l
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c
a
ti
o
n
,
v
o
l
.
2
8
,
p
p
.
2
5
8
1
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2
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2
0
1
7
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-
0
1
6
-
2
2
0
5
-
z
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2
0
1
7
.
[2
1
]
S
.
R.
Ba
n
d
e
k
a
r
a
n
d
C
.
Vijay
a
lak
s
h
m
i
,
“
De
sig
n
a
n
d
A
n
a
ly
sis
o
f
M
a
c
h
in
e
Lea
rn
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n
g
Alg
o
rit
h
m
s
f
o
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t
h
e
re
d
u
c
ti
o
n
o
f
c
rime
ra
tes
in
In
d
ia,”
Pr
o
c
e
d
ia
C
o
mp
u
ter
S
c
ien
c
e
,
v
o
l.
1
7
2
,
p
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2
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0
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0
,
d
o
i:
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0
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6
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.
p
ro
c
s.
2
0
2
0
.
0
5
.
0
1
8
.
[2
2
]
J.
Aru
n
a
d
e
v
i,
S
.
Ra
m
y
a
,
a
n
d
M
.
R.
Ra
ja,
“
A
stu
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tate
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h
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z
M
ira.
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