Record Class AssociationRule

java.lang.Object

java.lang.Record

smile.association.AssociationRule

Record Components:

antecedent - the antecedent itemset (LHS) of the association rule.

consequent - the consequent itemset (RHS) of the association rule.

support - the proportion of instances in the dataset that contain an itemset.

confidence - the percentage of instances that contain the consequent and antecedent together over the number of instances that only contain the antecedent.

lift - how many times more often antecedent and consequent occur together than expected if they were statistically independent.

leverage - the difference between the probability of the rule and the expected probability if the items were statistically independent.

public record AssociationRule(int[] antecedent, int[] consequent, double support, double confidence, double lift, double leverage)
extends Record

Association rule object. 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 as 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.

Lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. For an association rule X ⇒ Y, if the lift is equal to 1, it means that X and Y are independent. If the lift is higher than 1, it means that X and Y are positively correlated. If the lift is lower than 1, it means that X and Y are negatively correlated.

Constructor Summary

Constructors

Constructor	Description
AssociationRule(int[] antecedent, int[] consequent, double support, double confidence, double lift, double leverage)	Creates an instance of a AssociationRule record class.

Method Summary

Modifier and Type	Method	Description
int[]	antecedent()	Returns the value of the antecedent record component.
double	confidence()	Returns the value of the confidence record component.
int[]	consequent()	Returns the value of the consequent record component.
boolean	equals(Object o)	Two rules are considered equal if they have the same antecedent, consequent, support, and confidence.
int	hashCode()	Returns a hash code value for this object.
double	leverage()	Returns the value of the leverage record component.
double	lift()	Returns the value of the lift record component.
double	support()	Returns the value of the support record component.
String	toString()	Returns a string representation of this record class.

Methods inherited from class Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Details

AssociationRule

public AssociationRule(int[] antecedent,
 int[] consequent,
 double support,
 double confidence,
 double lift,
 double leverage)

Creates an instance of a AssociationRule record class.

Parameters:

antecedent - the value for the antecedent record component

consequent - the value for the consequent record component

support - the value for the support record component

confidence - the value for the confidence record component

lift - the value for the lift record component

leverage - the value for the leverage record component

Method Details

equals

public boolean equals(Object o)

Two rules are considered equal if they have the same antecedent, consequent, support, and confidence. lift and leverage are derived metrics and are intentionally excluded from equality.

Specified by:

equals in class Record

hashCode

public int hashCode()

Returns a hash code value for this object. The value is derived from the hash code of each of the record components.

Specified by:

hashCode in class Record

Returns:

a hash code value for this object

toString

public String toString()

Returns a string representation of this record class. The representation contains the name of the class, followed by the name and value of each of the record components.

Specified by:

toString in class Record

Returns:

a string representation of this object

antecedent

public int[] antecedent()

Returns the value of the antecedent record component.

Returns:

the value of the antecedent record component

consequent

public int[] consequent()

Returns the value of the consequent record component.

Returns:

the value of the consequent record component

support

public double support()

Returns the value of the support record component.

Returns:

the value of the support record component

confidence

public double confidence()

Returns the value of the confidence record component.

Returns:

the value of the confidence record component

lift

public double lift()

Returns the value of the lift record component.

Returns:

the value of the lift record component

leverage

public double leverage()

Returns the value of the leverage record component.

Returns:

the value of the leverage record component