Package elki.itemsetmining.associationrules.interest

Class PhiCorrelationCoefficient

java.lang.Object

elki.itemsetmining.associationrules.interest.PhiCorrelationCoefficient

All Implemented Interfaces:

InterestingnessMeasure

@Reference(authors="A. Agresti",title="Categorical Data Analysis",booktitle="Categorical Data Analysis",bibkey="books/wiley/Agresti90") @Reference(authors="P.-N. Tan, V. Kumar, J. Srivastava",title="Selecting the right objective measure for association analysis",booktitle="Information Systems 29.4",url="https://doi.org/10.1016/S0306-4379(03)00072-3",bibkey="DBLP:journals/is/TanKS04")
public class PhiCorrelationCoefficient
extends java.lang.Object
implements InterestingnessMeasure

Phi Correlation Coefficient interestingness measure.

\[ \frac{n P(X \cap Y) - P(X) - P(Y)}{\sqrt{P(X)P(Y)P(\neg X)P(\neg Y)}} \]

This is closely related to the χ² statistic.

The use for association rule mining was studied in:

P.-N. Tan, V. Kumar, J. Srivastava
Selecting the right objective measure for association analysis
Information Systems 29.4

Tan et al. attribute this measure to:

A. Agresti
Categorical Data Analysis
Wiley Series in Probability and Statistics

Since:

0.8.0

Author:

Abhishek Sharma

Constructor Summary

Constructors

Constructor	Description
PhiCorrelationCoefficient()	Constructor.

Method Summary

Modifier and Type	Method	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

PhiCorrelationCoefficient

public PhiCorrelationCoefficient()

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