- All Implemented Interfaces:
- java.lang.Iterable<AssociationRule>
public class ARM
extends java.lang.Object
implements java.lang.Iterable<AssociationRule>
Association Rule Mining.
Let I = {i1, i2,..., in} be a set of n
binary attributes called items. Let D = {t1, t2,..., tm}
be a set of transactions called the database. Each transaction in D has a
unique transaction ID and contains a subset of the items in I.
An association rule is defined as an implication of the form X ⇒ Y
where X, Y ⊆ I and X ∩ Y = Ø. The item sets X and Y are called
antecedent (left-hand-side or LHS) and consequent (right-hand-side or RHS)
of the rule, respectively. The support supp(X) of an item set X is defined as
the proportion of transactions in the database which contain the item set.
Note that the support of an association rule X ⇒ Y is supp(X ∪ Y).
The confidence of a rule is defined conf(X ⇒ Y) = supp(X ∪ Y) / supp(X).
Confidence can be interpreted as an estimate of the probability P(Y | X),
the probability of finding the RHS of the rule in transactions under the
condition that these transactions also contain the LHS.
Association rules are usually required to satisfy a user-specified minimum
support and a user-specified minimum confidence at the same time.