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

Class Jaccard

java.lang.Object

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

All Implemented Interfaces:

InterestingnessMeasure

@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") @Reference(authors="C. J. van Rijsbergen",title="Information Retrieval, 2nd Edition",booktitle="Butterworths, London, 1979",bibkey="DBLP:books/bu/Rijsbergen79")
public class Jaccard
extends java.lang.Object
implements InterestingnessMeasure

Jaccard interestingness measure:

\[\tfrac{\text{support}(A \cup B)}{\text{support}(A \cap B)} =\tfrac{P(A \cap B)}{P(A)+P(B)-P(A \cap B)} =\tfrac{P(A \cap B)}{P(A \cup B)}\]

Reference:

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

Tan et al. credit Rijsbergen for the use of Jaccard in Information Retrieval (it was not used for association rule mining here):

C. J. van Rijsbergen
Information Retrieval, 2nd Edition
Butterworths, London, 1979

Since:

0.7.5

Author:

Frederic Sautter

Constructor Summary

Constructors

Constructor and Description
Jaccard() 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

Jaccard

public Jaccard()

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