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27..54), and (Gender, female)
. The second criterion will indicate that (Gen-
der, female) is the best candidate. Thus
T
=
}
{
(Age, 27..33.5), (Gender,
female)
}
. Furthermore,
[
T
]
⊆
[(
Hobby,shooting
)]
.
We will execute the loop
for
to minimize
T
. After the first attempt we
have
[(
Gender,female
)]
⊆
[(
Hobby,shooting
)]
hence (Gender, female) is our second minimal complex. Additionally, for
T
=
{{
(Age, 40..63)
}
,
{
(Gender, female)
}}
,wehave
[(
Age,
40
..
63)]
∪
[(
Gender,female
)] = [(
Hobby,shooting
)]
,
so
is a local covering of [(Hobby, shooting)].
Our new input set to the algorithm MLEM2 is the other concept, i.e.,
the set
T
{
2, 5
}
. The set
T
(
G
) of all attribute-value pairs relevant to
G
is
{
(Age, 27..29.5), (Age, 29.5..63), (Age 27..33.5), (Age, 33.5..63), (Age, 27..40),
(Age, 27..54), (Gender, male)
. The most relevant attribute-value pairs are
(Age, 27..40), (Age, 27..54), and (Gender, male). The second criterion, the
minimum of
}
|
[(Attribute, value)]
|
does not break the tie since
|
[(Age, 27..40)]
|
=
|
[(Gender, male)]
|
= 3. The last resort is to select the first pair, i.e., (Age,
27..40). However,
[(
Age,
27
..
40)]
⊆
[(
Hobby,fishing
)]
,
therefore we have to run the MLEM2 algorithm through the second iteration
of the inner
while
loop. This time from
T
(
G
) the attribute-value pair (Age,
27..40) is excluded, and the most relevant attribute-value pair is (Gender,
male). Moreover, for
T
=
{
(
Age,
27
..
40)
,
(
Gender,male
)
}
we have
T
⊆
[(
Hobby,fishing
)]
.
Furthermore, this set is already minimal and
[
T
]=[(
Hobby,fishing
)]
,
so our local covering for [(Hobby, fishing)] is the set containing as the only
element
T
=
{
(Age, 27..40), (Gender, male)
}
. The rule set, determined by the
MLEM2 algorithm, in the LERS format, is
1, 2, 2
(Age, 40..63) -
>
(Hobby, shooting)
1, 2, 2
(Gender, female) -
>
(Hobby, shooting)
2, 2, 2
(Age, 27..40) & (Gender, male) -
>
(Hobby, fishing)
