de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest

Class Conviction

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Conviction

All Implemented Interfaces:

InterestingnessMeasure

@Reference(authors="S. Brin, R. Motwani, J. D Ullman, S Tsur",
           title="Dynamic itemset counting and implication rules for market basket data",
           booktitle="Proc. 1997 ACM SIGMOD international conference on management of data",
           url="https://doi.org/10.1145/253260.253325",
           bibkey="DBLP:conf/sigmod/BrinMUT97")
public class Conviction
extends java.lang.Object
implements InterestingnessMeasure

Conviction interestingness measure: \(\frac{P(X) P(\neg Y)}{P(X\cap\neg Y)}\).

Reference:

S. Brin, R. Motwani, J. D Ullman, S Tsur
Dynamic itemset counting and implication rules for market basket data
In Proc. 1997 ACM SIGMOD international conference on management of data

Since:

0.7.5

Author:

Frederic Sautter

Constructor Summary

Constructors

Constructor and Description
Conviction() Constructor.

Method Summary

Modifier and Type	Method and Description
double	measure(int t, int sX, int sY, int sXY) Computes the value of the measure for a given support values

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Conviction

public Conviction()

Constructor.

Method Detail

measure

public double measure(int t,
                      int sX,
                      int sY,
                      int sXY)

Description copied from interface: InterestingnessMeasure

Computes the value of the measure for a given support values

Specified by:

measure in interface InterestingnessMeasure

Parameters:

t - Total number of transaction

sX - Support of the antecedent

sY - Support of the consequent

sXY - Support of the union of antecedent and consequent

Returns:

value of the measure