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“4137X˙CH04˙Akerkar” — 2007/9/17 — 11:02 — page 144 — #26
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144 CHAPTER 4 Classification and Association
is built by repeatedly partitioning the training data, using criteria such as the goodness of the
split. The process is continued until all the records in a partition belong to the same class.
T is homogeneous: T contains cases all belonging to a single class C
j
. The decision
tree for T is a leaf identifying class C
j
.
T is not homogeneous: T contains cases that belong to a mixture of classes. A test is se-
lected, based on a single attribute, that has one or more mutually
exclusive outcomes {O
1
,O
2
, ..., O
n
}.T is partitioned into the sub-
sets T
1
,T
2
, ..., T , where T
j
contains all those cases in T that have
the outcome O
i
of the chosen test.