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P
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s th
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a
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to
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late
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In
d
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d
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re
d
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,
a
ss
o
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iatio
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,
c
las
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n
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e
stim
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ti
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t
h
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rtco
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m
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ften
a
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(fre
q
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m
to
m
a
x
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g
th
e
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isp
lay
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f
g
o
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s.
It
is
h
o
p
e
d
th
a
t
th
is
re
se
a
rc
h
c
a
n
b
e
u
se
d
t
o
a
d
ju
st
t
h
e
p
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d
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lay
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a
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rd
i
n
g
t
o
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h
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lev
e
l
o
f
fre
q
u
e
n
c
y
t
h
e
p
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t
is
so
u
g
h
t
b
y
t
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e
c
u
sto
m
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so
th
a
t
th
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u
sto
m
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h
a
s
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o
d
iffi
c
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f
in
d
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n
g
th
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p
ro
d
u
c
t
t
h
e
y
wa
n
t
.
K
ey
w
o
r
d
s
:
Data
m
in
in
g
Dis
p
lay
item
s
Fre
q
u
en
t p
atter
n
g
r
o
wth
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r
th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Asy
ah
r
i H
ad
i N
asy
u
h
a
I
n
f
o
r
m
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Sy
s
tem
STM
I
K
T
r
ig
u
n
a
Dh
a
r
m
a
A.
H
Nasu
tio
n
St.
No
.
7
3
Me
d
an
J
o
h
o
r
,
Me
d
an
,
I
n
d
o
n
esia
E
m
ail:
asy
ah
r
ih
ad
i@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Pro
d
u
cts
ar
e
g
o
o
d
s
th
at
ar
e
av
ailab
le
an
d
p
r
o
v
id
e
d
in
s
to
r
es
f
o
r
s
ale
[
1
]
.
Pro
d
u
cts
p
r
o
v
id
e
d
in
s
to
r
e
s
m
u
s
t
b
e
ar
r
an
g
ed
p
r
o
p
e
r
ly
to
a
ttra
ct
th
e
atten
tio
n
o
f
co
n
s
u
m
e
r
s
to
b
u
y
.
Pro
d
u
cts
ar
r
an
g
ed
in
a
s
to
r
e
will
d
ep
en
d
o
n
th
e
ty
p
e
o
f
s
to
r
e
[
2
]
.
T
h
e
ar
r
an
g
em
e
n
t
o
f
th
e
p
r
o
d
u
ct
is
o
n
e
th
in
g
th
at
is
n
o
t
less
im
p
o
r
tan
t,
b
ec
au
s
e
th
is
is
th
e
f
ir
s
t
im
p
r
ess
io
n
o
f
th
e
v
i
s
ito
r
o
f
th
e
s
to
r
e,
th
er
e
f
o
r
e
m
er
ch
an
d
is
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d
is
p
lay
ed
in
th
e
s
to
r
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o
o
m
m
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s
t
b
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ar
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an
g
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d
s
o
th
at
it
lo
o
k
s
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ea
t,
h
ar
m
o
n
io
u
s
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d
attr
ac
tiv
e
to
ev
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y
o
n
e,
esp
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th
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t
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d
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ex
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tis
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th
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an
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m
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t
o
f
g
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d
s
s
h
o
u
l
d
b
e
ch
a
n
g
ed
at
an
y
tim
e
s
o
it
is
n
o
t
b
o
r
in
g
a
n
d
a
d
ap
ted
to
t
h
e
s
itu
atio
n
.
Data
m
in
i
n
g
is
m
in
in
g
o
r
d
is
co
v
er
in
g
n
ew
in
f
o
r
m
atio
n
b
y
lo
o
k
in
g
f
o
r
ce
r
tain
p
atter
n
s
o
r
r
u
les
f
r
o
m
a
lar
g
e
am
o
u
n
t
o
f
d
ata
th
at
is
ex
p
ec
ted
to
o
v
er
c
o
m
e
th
ese
co
n
d
itio
n
s
[
3
]
.
Dat
a
m
in
in
g
is
a
b
r
an
ch
o
f
s
cien
ce
f
r
o
m
ar
tific
ial
in
tellig
en
ce
[
4
]
.
I
n
d
ata
m
in
in
g
th
er
e
ar
e
s
ev
er
al
ty
p
es
o
f
m
eth
o
d
s
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
F
r
eq
u
en
t p
a
tter
n
g
r
o
w
th
a
l
g
o
r
ith
m
fo
r
ma
ximizi
n
g
d
is
p
la
y
items
(
A
s
ya
h
r
i H
a
d
i Na
s
yu
h
a
)
391
b
y
th
e
u
s
e
in
clu
d
i
n
g
p
r
e
d
ictio
n
,
ass
o
ciatio
n
,
class
if
icatio
n
an
d
esti
m
atio
n
[
5
]
.
I
n
th
e
p
r
ed
i
ctio
n
m
eth
o
d
t
h
er
e
ar
e
s
ev
er
al
tech
n
iq
u
es
in
clu
d
i
n
g
th
e
f
r
e
q
u
en
t p
atter
n
g
r
o
wth
(
FP
-
g
r
o
wth
)
m
eth
o
d
.
FP
-
g
r
o
wth
alg
o
r
ith
m
is
th
e
d
ev
elo
p
m
e
n
t
o
f
t
h
e
a
p
r
io
r
i
al
g
o
r
ith
m
[
6
]
.
So
,
th
e
s
h
o
r
tc
o
m
in
g
s
o
f
th
e
ap
r
io
r
i
alg
o
r
ith
m
ar
e
co
r
r
ec
ted
b
y
th
e
FP
-
g
r
o
wth
alg
o
r
ith
m
[
7
]
.
FP
-
g
r
o
wth
is
o
n
e
alter
n
ativ
e
alg
o
r
it
h
m
th
at
ca
n
b
e
u
s
ed
to
d
eter
m
in
e
th
e
s
et
o
f
d
ata
th
at
m
o
s
t o
f
ten
a
p
p
ea
r
s
(
f
r
eq
u
en
t item
s
et)
in
a
d
ata
s
et
[
8
]
.
2.
RE
S
E
ARCH
M
E
T
H
O
DS
2
.
1
.
Da
t
a
m
ini
ng
T
h
e
ter
m
d
ata
m
in
in
g
h
as
s
ev
e
r
al
v
iews,
s
u
ch
as
k
n
o
wled
g
e
d
is
co
v
er
y
o
r
p
atter
n
r
ec
o
g
n
itio
n
[
9
]
.
B
o
th
o
f
th
ese
ter
m
s
h
av
e
th
eir
r
esp
e
ctiv
e
ac
cu
r
ac
y
.
T
h
e
ter
m
k
n
o
wled
g
e
d
is
co
v
er
y
is
ap
p
r
o
p
r
ia
te
b
ec
au
s
e
th
e
m
ain
p
u
r
p
o
s
e
o
f
d
ata
m
in
in
g
is
to
g
et
k
n
o
wled
g
e
th
at
is
s
till
h
id
d
en
in
ch
u
n
k
s
o
f
d
ata
[
1
0
]
.
T
h
e
ter
m
p
atter
n
r
ec
o
g
n
itio
n
is
also
ap
p
r
o
p
r
iat
e
f
o
r
u
s
e
b
ec
au
s
e
th
e
k
n
o
wled
g
e
to
b
e
ex
tr
ac
te
d
d
o
es
in
d
e
ed
tak
e
th
e
f
o
r
m
o
f
p
atter
n
s
th
at
m
ay
also
s
till
n
ee
d
to
b
e
e
x
tr
ac
ted
f
r
o
m
in
s
id
e
t
h
e
ch
u
n
k
o
f
d
ata
b
ei
n
g
f
ac
e
d
.
2
.
2
.
P
ha
s
e
o
f
da
t
a
m
ini
ng
Kn
o
wled
g
e
d
is
co
v
er
y
in
d
ata
b
ase
(
KDD)
is
th
e
p
r
o
ce
s
s
o
f
d
eter
m
in
i
n
g
in
f
o
r
m
atio
n
th
at
s
er
v
es
to
d
eter
m
in
e
th
e
p
atter
n
s
co
n
tain
ed
in
d
ata
[
1
1
]
.
T
h
is
in
f
o
r
m
atio
n
is
co
n
tain
ed
in
a
lar
g
e
d
atab
ase
th
at
was
p
r
ev
io
u
s
ly
u
n
k
n
o
wn
an
d
p
o
ten
tially
u
s
ef
u
l.
Data
m
in
in
g
is
o
n
e
s
tep
in
a
s
er
ies
o
f
KDD
iter
at
iv
e
p
r
o
ce
s
s
es
[
1
2
]
.
T
h
e
s
ta
g
es
o
f
th
e
KDD
p
r
o
ce
s
s
co
n
s
is
t
o
f
[
1
3
-
1
5
]
:
d
a
ta
s
elec
tio
n
,
p
re
-
p
r
o
ce
s
s
in
g
an
d
clea
n
in
g
d
ata
,
t
r
an
s
f
o
r
m
atio
n
,
d
ata
m
in
in
g
,
a
n
d
i
n
ter
p
r
etatio
n
/
e
v
alu
atio
n
.
D
ata
m
in
in
g
is
o
n
e
o
f
th
e
KDD
s
er
ies
o
f
k
n
o
wled
g
e.
KDD
is
th
e
p
r
o
ce
s
s
o
f
d
eter
m
in
in
g
u
s
ef
u
l
in
f
o
r
m
atio
n
an
d
p
atter
n
s
in
d
ata.
Data
m
in
in
g
is
o
n
e
s
tep
o
f
an
iter
ativ
e
KDD
p
r
o
ce
s
s
.
KDD
s
tag
es p
r
o
ce
s
s
as sh
o
wn
in
th
e
f
o
llo
win
g
Fig
u
r
e
1
.
Fig
u
r
e
1
.
KDD
2
.
3
.
Da
t
a
m
ini
ng
t
ec
hn
iqu
es
So
m
e
d
ata
m
in
in
g
tech
n
iq
u
es a
r
e
as f
o
llo
ws
[
1
6
-
18]
:
−
C
las
s
if
icatio
n
:
A
s
s
ig
n
in
g
a
n
ew
d
ata
r
ec
o
r
d
to
o
n
e
o
f
s
o
m
e
p
r
e
-
d
ef
i
n
ed
ca
teg
o
r
ies (
o
r
clas
s
es).
−
R
eg
r
ess
io
n
:
Pre
d
ict
th
e
v
alu
e
o
f
a
g
iv
en
co
n
tin
u
o
u
s
v
ar
iab
le
b
ased
o
n
th
e
v
alu
e
o
f
an
o
th
er
v
ar
iab
le,
ass
u
m
in
g
a
lin
ea
r
o
r
n
o
n
lin
ea
r
d
ep
e
n
d
en
c
y
m
o
d
el.
T
h
is
tec
h
n
iq
u
e
is
wid
ely
s
tu
d
ied
in
s
t
atis
tics
,
th
e
f
ield
o
f
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
n
eu
r
al
n
etwo
r
k
s
)
.
−
C
lu
s
ter
in
g
:
Par
tit
io
n
d
ata
s
et
in
to
s
ev
er
al
s
u
b
-
s
ets
o
r
g
r
o
u
p
s
in
s
u
ch
a
way
s
o
th
at
elem
en
t
s
o
f
a
p
ar
ticu
la
r
g
r
o
u
p
h
av
e
s
h
ar
e
d
p
r
o
p
er
ty
s
ets
to
g
eth
er
,
with
a
h
ig
h
lev
el
o
f
s
im
ilar
ity
in
o
n
e
g
r
o
u
p
an
d
a
lo
w
lev
el
o
f
s
im
ilar
ity
b
etwe
en
g
r
o
u
p
s
.
Als
o
ca
lled
'
u
n
s
u
p
er
v
is
ed
ler
n
i
ng
'
.
−
Ass
o
ciatio
n
r
u
le
s
:
Dete
cts
a
co
llectio
n
o
f
attr
ib
u
tes
th
at
ap
p
ea
r
to
g
eth
er
(
co
-
o
cc
u
r
)
o
n
a
f
r
eq
u
en
t
f
r
eq
u
en
cy
,
an
d
f
o
r
m
a
n
u
m
b
e
r
o
f
r
u
les o
f
th
e
co
llectio
n
.
−
Sear
ch
f
o
r
s
eq
u
en
tial
p
atter
n
s
(
s
eq
u
en
ce
m
in
in
g
)
:
L
o
o
k
f
o
r
a
n
u
m
b
er
o
f
ev
e
n
ts
th
at
g
en
e
r
ally
o
cc
u
r
to
g
eth
er
.
I
f
g
iv
e
n
a
s
et
o
f
o
b
jects,
with
ea
ch
o
b
ject
ass
o
ciate
d
with
th
e
tim
e
o
f
o
cc
u
r
r
en
ce
,
th
en
g
et
a
p
atter
n
th
at
p
r
ed
icts
s
tr
o
n
g
s
eq
u
e
n
tial d
ep
en
d
en
cies b
etwe
en
d
i
f
f
er
en
t e
v
en
ts
.
2
.
4
.
FP
-
g
ro
wt
h
FP
-
g
r
o
wth
alg
o
r
ith
m
is
th
e
d
e
v
elo
p
m
en
t
o
f
ap
r
io
r
i
alg
o
r
ith
m
[
1
9
]
.
So
,
th
e
s
h
o
r
tco
m
in
g
s
o
f
th
e
ap
r
io
r
i
alg
o
r
ith
m
ar
e
c
o
r
r
ec
ted
b
y
th
e
FP
-
g
r
o
wth
alg
o
r
ith
m
.
FP
-
g
r
o
wth
is
o
n
e
alter
n
ativ
e
alg
o
r
ith
m
th
at
ca
n
b
e
u
s
e
d
to
d
eter
m
i
n
e
th
e
s
et
o
f
d
ata
th
at
m
o
s
t
o
f
ten
a
p
p
e
ar
s
(
f
r
e
q
u
en
t
item
s
et)
in
a
d
ata
s
et
[
2
0
]
.
Ap
r
io
r
i
alg
o
r
ith
m
r
eq
u
ir
es
g
e
n
er
atin
g
ca
n
d
id
ates
to
g
et
f
r
eq
u
e
n
t
item
s
et.
Ho
we
v
er
,
th
e
FP
-
g
r
o
wth
alg
o
r
ith
m
g
en
er
ates
ca
n
d
id
ates
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
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
,
Vo
l.
19
,
No
.
2
,
Ap
r
il
2
0
2
1
:
3
9
0
-
39
6
392
n
o
t
d
o
n
e
b
ec
au
s
e
FP
-
g
r
o
wth
u
s
es
th
e
co
n
ce
p
t
o
f
tr
ee
d
ev
elo
p
m
en
t
in
s
ea
r
ch
in
g
f
o
r
f
r
e
q
u
en
t
item
s
[
2
1
]
.
T
h
is
is
wh
at
ca
u
s
es th
e
FP
-
g
r
o
wth
alg
o
r
ith
m
to
b
e
f
aster
th
an
th
e
ap
r
io
r
i
alg
o
r
ith
m
[
2
2
]
.
T
h
e
ch
ar
ac
ter
is
tic
o
f
FP
-
g
r
o
w
th
alg
o
r
ith
m
is
th
e
d
ata
s
tr
u
ctu
r
e
u
s
ed
is
a
tr
ee
ca
lled
FP
-
tr
ee
[
2
3
]
.
B
y
u
s
in
g
FP
-
tr
ee
,
th
e
FP
-
g
r
o
wth
alg
o
r
ith
m
ca
n
d
ir
ec
tly
ex
tr
ac
t
f
r
eq
u
e
n
t
I
tem
s
et
f
r
o
m
F
P
-
tr
ee
[
2
4
]
.
E
x
tr
ac
tin
g
f
r
eq
u
e
n
t
item
s
et
u
s
in
g
th
e
FP
-
g
r
o
wth
alg
o
r
ith
m
will
b
e
d
o
n
e
b
y
g
en
e
r
atin
g
a
d
ata
tr
ee
s
tr
u
ctu
r
e
o
r
ca
lled
FP
-
tr
ee
.
Af
ter
th
e
FP
-
tr
ee
d
e
v
elo
p
m
en
t
s
tag
e
o
f
a
s
et
o
f
t
r
an
s
ac
tio
n
d
a
ta,
th
e
FP
-
g
r
o
wt
h
alg
o
r
ith
m
will
b
e
ap
p
lied
to
lo
o
k
f
o
r
s
ig
n
if
ica
n
t
f
r
eq
u
e
n
t item
s
et
[
2
5
]
.
T
h
e
FP
-
g
r
o
wth
alg
o
r
ith
m
is
d
iv
id
ed
in
to
th
r
ee
m
ain
s
tep
s
[
2
6
]
:
1
)
C
o
n
d
itio
n
al
p
at
ter
n
b
ase
;
2
)
C
o
n
d
itio
n
al
FP
-
tr
ee
; 3
)
Fre
q
u
en
t
item
s
et
.
T
h
e
f
o
r
m
o
f
th
e
FP
-
g
r
o
wth
a
lg
o
r
ith
m
is
as f
o
l
lo
ws:
I
n
p
u
t: FP
-
tr
ee
tr
ee
Ou
tp
u
t: R
t A
co
m
p
lete
s
et
o
f
f
r
eq
u
en
t
p
atter
n
s
Me
th
o
d
: FP
-
g
r
o
wth
(
tr
ee
,
n
u
ll)
Pro
ce
d
u
r
e:
FP
-
g
r
o
wth
(
tr
ee
,
α
)
{
0
1
: I
f
T
r
ee
co
n
tain
s
s
in
g
le
p
at
h
P;
0
2
: T
h
en
f
o
r
ea
ch
co
m
b
in
atio
n
(
n
o
tated
β)
o
f
t
h
e
n
o
d
es in
t
h
e
d
o
p
ath
0
3
: G
en
er
ate
th
e
B
u
ild
p
atter
n
β α
with
s
u
p
p
o
r
t f
r
o
m
n
o
d
es in
th
e
d
o
β p
ath
0
4
: E
ls
e
f
o
r
ea
c
h
a1
i
n
th
e
h
ea
d
er
o
f
t
h
e
d
o
tr
ee
{
0
5
: g
en
e
r
ate
p
atter
n
0
6
: w
ak
e
u
p
β =
a1
α
with
s
u
p
p
o
r
t =
a1
s
u
p
p
o
r
t
0
7
: I
f
tr
ee
β
3.
ANALY
SI
S A
ND
RE
SU
L
T
3
.
1
.
P
ro
blem
a
na
l
y
s
is
Pro
b
lem
an
aly
s
is
is
a
p
r
o
ce
s
s
t
h
at
in
v
o
lv
es a
s
u
r
v
ey
o
f
th
e
cu
r
r
en
t sy
s
tem
an
d
an
aly
s
is
o
f
u
s
er
n
ee
d
s
.
Af
ter
co
n
d
u
ctin
g
in
ter
v
iews
a
n
d
r
e
v
iewin
g
d
o
cu
m
en
ts
.
T
h
e
n
ex
t
s
tag
e
is
to
d
escr
ib
e
th
e
s
y
s
tem
th
at
will
b
e
d
esig
n
ed
in
t
h
e
f
o
r
m
o
f
f
lo
w,
a
s
well
as
u
n
if
ied
m
o
d
elin
g
lan
g
u
ag
e
(
UM
L
)
.
Fro
m
t
h
e
r
esu
lt
s
o
f
th
is
d
escr
ip
tio
n
ca
n
b
e
s
ee
n
wh
at
is
n
ee
d
e
d
f
o
r
th
e
d
ev
el
o
p
m
en
t
o
f
th
e
s
y
s
te
m
s
o
th
at
th
e
s
y
s
tem
is
d
esig
n
ed
to
r
u
n
p
er
f
ec
tly
as d
esire
d
.
3
.
2
.
Sy
s
t
e
m
a
lg
o
rit
hm
FP
-
g
r
o
wth
is
a
f
r
eq
u
en
t
item
s
et
s
ea
r
ch
alg
o
r
ith
m
th
at
is
o
b
tain
ed
f
r
o
m
t
h
e
FP
-
tr
ee
b
y
ex
p
lo
r
in
g
th
e
tr
ee
f
r
o
m
b
o
tto
m
to
t
o
p
.
FP
-
g
r
o
wth
is
th
e
d
e
v
elo
p
m
e
n
t
o
f
ap
r
io
r
i
al
g
o
r
ith
m
.
T
h
is
alg
o
r
it
h
m
d
ete
r
m
in
es
th
e
f
r
eq
u
e
n
t
item
s
et
th
at
en
d
s
in
a
p
ar
ticu
lar
s
u
f
f
ix
b
y
u
s
in
g
th
e
d
ev
id
e
an
d
co
n
q
u
er
m
eth
o
d
to
b
r
ea
k
th
e
p
r
o
b
lem
in
to
s
m
aller
s
u
b
p
r
o
b
lem
s
.
I
n
th
e
f
o
r
m
atio
n
o
f
FP
-
g
r
o
wth
,
n
am
ely
th
r
o
u
g
h
th
e
f
o
llo
win
g
alg
o
r
ith
m
:
−
Fo
r
m
atio
n
o
f
FP
-
tr
ee
.
a.
Dete
r
m
in
e
tr
an
s
ac
tio
n
d
ata
.
b
.
C
o
u
n
t th
e
a
m
o
u
n
t p
er
item
.
c.
Dete
r
m
in
e
item
s
th
at
m
ee
t t
h
e
m
in
im
u
m
s
u
p
p
o
r
t v
alu
e≥
2
0
%
.
d
.
Dete
r
m
in
e
tr
a
n
s
ac
tio
n
d
ata
th
at
co
n
tain
s
m
in
im
u
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3
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[
8
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.
At
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25
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t
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Af
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o
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FP
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ar
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lo
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k
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f
o
r
th
e
f
r
eq
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en
t
item
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et,
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ich
is
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u
m
m
ar
ized
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th
e
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ab
le
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.
Af
ter
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ee
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ir
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h
en
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e
co
n
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lcu
lated
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ased
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n
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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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
F
r
eq
u
en
t p
a
tter
n
g
r
o
w
th
a
l
g
o
r
ith
m
fo
r
ma
ximizi
n
g
d
is
p
la
y
items
(
A
s
ya
h
r
i H
a
d
i Na
s
yu
h
a
)
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o
n
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→
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mb
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of
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r
ans
ac
ti
on
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3.
5
.
F
o
rma
t
io
n o
f
a
s
s
o
cia
t
io
n r
ules
Ass
o
ciatio
n
r
u
le
is
a
m
eth
o
d
t
h
at
aim
s
to
f
in
d
p
atter
n
s
th
at
o
f
ten
a
p
p
ea
r
b
etwe
en
m
an
y
tr
an
s
ac
tio
n
s
,
wh
er
e
ea
ch
tr
a
n
s
ac
tio
n
co
n
s
is
ts
o
f
s
ev
er
al
item
s
s
o
th
at
th
is
m
eth
o
d
will
co
n
tain
a
r
ec
o
m
m
en
d
atio
n
s
y
s
tem
th
r
o
u
g
h
f
in
d
i
n
g
p
atter
n
s
b
etwe
en
item
s
in
f
r
eq
u
en
t
tr
an
s
ac
tio
n
s
.
On
ly
co
m
b
in
atio
n
s
g
r
ea
ter
th
an
o
r
eq
u
al
to
th
e
m
in
im
u
m
co
n
f
id
en
ce
will b
e
u
s
ed
to
f
o
r
m
a
r
u
le,
th
e
r
u
le
ca
n
b
e
s
ee
n
in
T
a
b
le
8.
T
ab
le
8
.
Stro
n
g
ass
o
ciatio
n
r
u
le
No
R
u
l
e
S
u
p
p
o
r
t
%
C
o
n
f
i
d
e
n
c
e
%
1
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u
c
u
k
,
C
h
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t
a
t
o
1
5
%
6
0
%
2
P
u
c
u
k
,
S
i
l
v
e
r
q
u
e
e
n
2
5
%
1
0
0
%
3
C
h
i
t
a
t
o
,
S
i
l
v
e
r
q
u
e
e
n
2
5
%
8
3
%
4
C
h
i
t
a
t
o
,
S
a
r
i
R
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t
i
1
0
%
3
3
%
5
S
a
r
i
R
o
t
i
,
S
i
l
v
e
r
q
u
e
e
n
2
5
%
1
0
0
%
6
S
a
r
i
R
o
t
i
,
C
h
i
t
a
t
o
1
0
%
4
0
%
7
S
a
r
i
R
o
t
i
,
P
u
c
u
k
5
%
2
0
%
8
C
i
t
r
a
,
P
o
p
M
i
e
K
a
r
i
2
0
%
1
0
0
%
9
D
a
i
a
,
C
i
t
r
a
1
5
%
7
5
%
4.
CO
NCLU
SI
O
N
F
P
-
g
r
o
wth
is
a
m
eth
o
d
th
at
c
an
p
r
o
ce
s
s
tr
an
s
ac
tio
n
d
ata
m
o
r
e
q
u
ick
l
y
an
d
ac
cu
r
ately
.
T
h
is
m
eth
o
d
ca
n
also
b
e
u
s
ed
to
an
aly
ze
s
ales
d
ata
b
y
d
eter
m
in
in
g
th
e
ty
p
es
o
f
p
r
o
d
u
cts
a
n
d
t
r
an
s
ac
tio
n
s
,
d
esig
n
in
g
s
ales
d
ata
g
r
o
u
p
in
g
s
y
s
tem
s
.
T
h
is
m
eth
o
d
ca
n
b
e
u
s
ed
to
ar
r
an
g
e
p
r
o
d
u
ct
ap
p
ea
r
an
ce
i
n
o
r
d
er
to
attr
ac
t
cu
s
to
m
er
s
an
d
in
cr
ea
s
e
s
ales.
RE
F
E
R
E
NC
E
[1
]
T.
A.
M
u
rit
a
la,
“
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
l
o
f
A
d
v
a
n
c
e
s
i
n
M
a
n
a
g
e
m
e
n
t
a
n
d
Ec
o
n
o
m
ics
”
,
I
n
ter
n
a
ti
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n
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l
J
.
Ad
v
.
M
a
n
a
g
.
Eco
n
.
,
v
o
l
.
1
,
n
o
.
5
,
p
p
.
1
1
6
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,
2
0
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.
Av
a
il
a
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le
:
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w.m
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[2
]
A.
F
.
C
o
o
p
e
r
a
n
d
R.
T
h
a
k
u
r
,
“
Th
e
g
ro
u
p
o
f
twe
n
t
y
(G
2
0
),
”
T
h
e
Gr
o
u
p
o
f
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we
n
ty (G
2
0
)
,
p
p
.
1
-
1
9
4
,
2
0
1
3
.
[3
]
N.
Bh
a
tl
a
a
n
d
K.
Jy
o
ti
,
“
An
a
n
a
ly
sis o
f
h
e
a
rt
d
ise
a
se
p
re
d
ictio
n
u
si
n
g
d
if
fe
re
n
t
d
a
ta m
in
in
g
tec
h
n
i
q
u
e
s,”
In
t.
J
.
En
g
.
Res
.
T
e
c
n
o
l.
,
v
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l.
1
,
n
o
.
8
,
p
p
.
1
-
4
,
2
0
1
2
.
[4
]
M
.
De
y
a
n
d
S
.
S
.
Ra
u
tara
y
,
“
S
t
u
d
y
a
n
d
An
a
ly
sis
o
f
Da
t
a
m
in
i
n
g
Al
g
o
rit
h
m
s
f
o
r
He
a
lt
h
c
a
re
De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
,
”
In
t.
J
.
Co
m
p
u
t.
S
c
i.
In
f.
T
e
c
h
n
o
l
.
,
v
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l
.
5
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n
o
.
1
,
p
p
.
4
7
0
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4
7
7
,
2
0
1
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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[5
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M
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Vo
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“
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n
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[6
]
B.
S
.
Ku
m
a
r
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K.V.R
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m
a
n
i,
“
Im
p
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tatio
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Us
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ro
wth
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m
s,”
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J
.
A
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p
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.
4
0
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[7
]
K.
Dh
a
rm
a
ra
jan
a
n
d
M
.
A.
Do
ra
i
ra
n
g
a
sw
a
m
y
,
“
An
a
ly
sis
o
f
F
P
-
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wth
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d
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ri
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m
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fro
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a
ta,”
2
0
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EE
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[8
]
A.
Ik
h
wa
n
,
“
A
No
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ty
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Da
ta
M
in
i
n
g
fo
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F
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ro
wth
Al
g
o
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h
m
,
”
In
t.
J
.
Civ.
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n
g
.
T
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,
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l
.
9
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1
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.
[9
]
M
.
S
.
Ch
e
n
,
J.
Ha
n
,
a
n
d
P
.
S
.
Y
u
,
“
Da
ta
m
in
i
n
g
:
An
o
v
e
rv
iew
fr
o
m
a
d
a
tab
a
se
p
e
rsp
e
c
ti
v
e
,
”
IEE
E
T
ra
n
s.
Kn
o
wl.
Da
ta
E
n
g
.
,
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l
.
8
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6
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p
p
.
8
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6
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.
[1
0
]
A.
Ho
lzi
n
g
e
r
a
n
d
I.
Ju
risica
,
“
Kn
o
wle
d
g
e
d
isc
o
v
e
ry
a
n
d
d
a
ta
m
i
n
in
g
i
n
b
i
o
m
e
d
ica
l
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n
fo
rm
a
ti
c
s:
Th
e
f
u
tu
re
is
i
n
in
teg
ra
ti
v
e
,
in
tera
c
ti
v
e
m
a
c
h
in
e
l
e
a
rn
in
g
so
l
u
ti
o
n
s,”
L
e
c
t.
No
tes
C
o
mp
u
t
.
S
c
i.
(i
n
c
lu
d
in
g
S
u
b
se
r.
L
e
c
t.
No
te
s
Arti
f.
In
tell.
L
e
c
t.
No
tes
Bi
o
in
fo
rm
a
t
ics
)
,
v
o
l.
8
4
0
1
,
p
p
.
1
-
1
8
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2
0
1
4
.
[1
1
]
Zu
lh
a
m
a
n
d
As
y
a
h
ri
Ha
d
i
Na
sy
u
h
a
,
“
Ap
p
li
c
a
ti
o
n
o
f
Da
ta M
i
n
in
g
f
o
r
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a
n
a
G
ro
u
p
i
n
g
(I
n
Ba
h
a
sa
:
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e
n
e
ra
p
a
n
Da
ta
M
in
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n
g
U
n
tu
k
P
e
n
g
e
lo
m
p
o
k
a
n
W
a
h
a
n
a
)
,”
S
a
in
s
d
a
n
Ko
m
p
u
ter
(
S
A
INT
IKOM
)
,
v
o
l
.
1
7
,
n
o
.
1
,
p
p
.
9
2
–
1
0
4
,
2
0
1
8
.
[1
2
]
C.
B
o
h
m
a
n
d
F
.
Kre
b
s,
“
Th
e
k
-
Ne
a
re
st
N
e
ig
h
b
o
u
r
Jo
i
n
:
T
u
rb
o
Ch
a
rg
in
g
th
e
KD
D
P
ro
c
e
ss
,
”
Kn
o
wl.
In
f
.
S
y
st.
,
v
o
l.
6
,
n
o
.
6
,
p
p
.
7
2
8
-
7
4
9
,
2
0
0
4
.
[1
3
]
G
.
W.
An
d
,
G
.
J.
Wi
ll
iam
s,
a
n
d
Z
.
Hu
a
n
g
,
“
A Cas
e
S
t
u
d
y
i
n
K
n
o
wl
e
d
g
e
Ac
q
u
isit
io
n
f
o
r
I
n
su
ra
n
c
e
Ri
sk
As
se
ss
m
e
n
t
u
sin
g
a
KD
D
M
e
th
o
d
o
lo
g
y
,
”
P
r
e
se
n
ted
a
t
PKA
W
9
6
,
th
e
Pa
c
if
ic
Ri
m
Kn
o
wled
g
e
Acq
u
isi
ti
o
n
W
o
rk
sh
o
p
,
S
y
d
n
e
y
,
Au
stra
li
a
,
1
9
9
6
,
p
p
.
1
1
7
-
1
2
9
.
[1
4
]
O.
M
a
imo
n
a
n
d
L.
Ro
k
a
c
h
,
“
Da
ta
M
in
i
n
g
a
n
d
Kn
o
wle
d
g
e
Disc
o
v
e
r
y
Ha
n
d
b
o
o
k
,
”
Da
t
a
M
i
n
.
K
n
o
wl.
Disc
o
v
.
Ha
n
d
b
.
,
2
0
1
0
,
p
p
.
1
-
15
.
[1
5
]
H.
M
.
C
h
u
n
g
a
n
d
P
.
G
ra
y
,
“
S
p
e
c
i
a
l
se
c
ti
o
n
:
Da
ta m
in
i
n
g
,
”
J
.
M
a
n
a
g
.
I
n
f.
S
y
st.
,
v
o
l.
1
6
,
n
o
.
1
,
p
p
.
1
1
-
16
,
1
9
9
9
.
[1
6
]
E.
B.
C
o
sta
,
B.
F
o
n
se
c
a
,
M
.
A.
S
a
n
tan
a
,
F
.
F
.
d
e
Ara
ú
j
o
,
a
n
d
J.
R
e
g
o
,
“
E
v
a
lu
a
ti
n
g
th
e
e
ffe
c
ti
v
e
n
e
s
s
o
f
e
d
u
c
a
ti
o
n
a
l
d
a
ta
m
in
in
g
tec
h
n
i
q
u
e
s
f
o
r
e
a
rly
p
re
d
icti
o
n
o
f
stu
d
e
n
ts’
a
c
a
d
e
m
i
c
fa
il
u
re
i
n
i
n
tro
d
u
c
t
o
ry
p
r
o
g
ra
m
m
in
g
c
o
u
rse
s,”
Co
mp
u
t
.
Hu
m
a
n
Beh
a
v
.
,
v
o
l.
7
3
,
p
p
.
2
4
7
-
2
5
6
,
2
0
1
7
.
[1
7
]
A.
Bh
a
rd
wa
j,
A.
S
h
a
rm
a
,
a
n
d
V.
K.
S
h
ri
v
a
sta
v
a
,
“
Da
ta
M
in
in
g
Tec
h
n
i
q
u
e
s
a
n
d
Th
e
ir
Im
p
lem
e
n
tatio
n
in
Bl
o
o
d
Ba
n
k
S
e
c
to
r
–
A Re
v
iew
,
”
In
t.
J
.
E
n
g
.
Res
.
Ap
p
l.
,
v
o
l
.
2
,
n
o
.
A
u
g
u
st,
p
p
.
1
3
0
3
-
1
3
0
9
,
2
0
1
2
.
[1
8
]
K.S
rin
i
v
a
s,
B.
K.
Ra
n
i
,
a
n
d
A
.
G
o
v
r
d
h
a
n
,
“
Ap
p
li
c
a
ti
o
n
s
o
f
d
a
ta
m
in
in
g
tec
h
n
iq
u
e
s
i
n
h
e
a
lt
h
c
a
re
a
n
d
p
re
d
icti
o
n
o
f
h
e
a
rt
a
tt
a
c
k
s
,
”
In
t.
J
.
Co
mp
u
t.
S
c
i
.
En
g
.
,
v
o
l.
0
2
,
n
o
.
0
2
,
p
p
.
2
5
0
-
2
5
5
,
2
0
1
0
.
[1
9
]
D.
Hu
n
y
a
d
i,
“
P
e
rfo
rm
a
n
c
e
c
o
m
p
a
riso
n
o
f
A
p
rio
r
i
a
n
d
F
P
-
G
ro
wth
a
lg
o
rit
h
m
s in
g
e
n
e
ra
ti
n
g
a
ss
o
c
iati
o
n
ru
les
,
”
Pro
c
.
Eu
r.
Co
m
p
u
t
.
Co
n
f.
ECC
’1
1
,
2
0
1
1
,
p
p
.
3
7
6
-
3
8
1
.
[2
0
]
H.
Li
,
Y.
Wan
g
,
D.
Z
h
a
n
g
,
M
.
Zh
a
n
g
,
a
n
d
E.
Y.
Ch
a
n
g
,
“
P
F
P
:
P
a
ra
ll
e
l
F
P
-
g
r
o
wth
f
o
r
q
u
e
ry
re
c
o
m
m
e
n
d
a
ti
o
n
,
”
Rec
S
y
s’0
8
Pro
c
.
2
0
0
8
ACM
Co
n
f
.
Rec
o
mm
.
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y
st.
,
2
0
0
8
,
p
p
.
1
0
7
-
1
1
4
.
[2
1
]
A.
M
.
S
a
i
d
,
P
.
D.
D.
D
o
m
in
ic,
a
n
d
A.
B.
A
b
d
u
ll
a
h
,
“
A
c
o
m
p
a
ra
ti
v
e
stu
d
y
o
f
F
P
-
g
r
o
wth
v
a
riatio
n
s,”
In
t.
J
.
C
o
mp
u
t.
S
c
i.
Ne
tw.
S
e
c
u
r.
,
v
o
l
.
9
,
n
o
.
5
,
p
p
.
2
6
6
-
2
7
2
,
2
0
0
9
.
[2
2
]
J.
He
a
to
n
,
“
Co
m
p
a
rin
g
d
a
tas
e
t
c
h
a
ra
c
teristics
th
a
t
fa
v
o
r
t
h
e
Ap
ri
o
ri,
Ecla
t
o
r
F
P
-
G
ro
wth
fr
e
q
u
e
n
t
it
e
m
se
t
m
in
in
g
a
lg
o
rit
h
m
s,”
Co
n
f.
Pro
c
.
-
IEE
E
S
OU
T
HEAS
T
CON
,
v
o
l
.
2
0
1
6
-
Ju
l
y
,
2
0
1
6
.
[2
3
]
Y.
G
.
S
u
c
a
h
y
o
a
n
d
R.
P
.
G
o
p
a
lan
,
“
CT
-
P
RO:
A
B
o
tt
o
m
-
Up
N
o
n
Re
c
u
rsiv
e
F
re
q
u
e
n
t
Item
se
t
M
in
in
g
Al
g
o
ri
th
m
Us
in
g
Co
m
p
re
ss
e
d
F
P
-
Tree
Da
ta
S
tru
c
tu
re
,
”
Co
n
fer
e
n
c
e
:
FIM
I
'0
4
,
Pro
c
e
e
d
i
n
g
s
o
f
th
e
IE
EE
ICD
M
W
o
rk
sh
o
p
o
n
Fre
q
u
e
n
t
Item
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