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AssociationRuleLearning

Guides

  • Association rule learning

Symbols

  • Apriori
  • Eclat
AntonAntonov`AssociationRuleLearning`
Eclat
​
Eclat
[data,minSupport,opts]
finds frequent sets using the Eclat algorithm
​
Details and Options

Examples  
(3)
Basic Examples  
(1)
Here we get the Titanic dataset:
In[1]:=
dsTitanic=ResourceFunction["ExampleDataset"][{"MachineLearning","Titanic"}];​​dsTitanic=dsTitanic[All,KeyMap[StringReplace[#,"passenger """]&,#]&];​​dsTitanic[1;;4]
Out[1]=
class
age
sex
survival
1st
29.0
female
survived
1st
0.9167
male
survived
1st
2.0
female
died
1st
30.0
male
died
Here we find the frequent sets of variable values in the Titanic data that appear more than 100 times:
In[2]:=
aRes=
Eclat
[dsTitanic[All,KeyDrop[#,"age"]&],100]
Out[2]=
{class.1st}323,{class.1st,sex.female}144,{class.1st,sex.female,survival.survived}139,{class.1st,sex.male}179,{class.1st,sex.male,survival.died}118,{class.1st,survival.died}123,{class.1st,survival.survived}200,{class.2nd}277,{class.2nd,sex.female}106,{class.2nd,sex.male}171,{class.2nd,sex.male,survival.died}146,{class.2nd,survival.died}158,{class.2nd,survival.survived}119,{class.3rd}709,{class.3rd,sex.female}216,{class.3rd,sex.female,survival.died}110,{class.3rd,sex.female,survival.survived}106,{class.3rd,sex.male}493,{class.3rd,sex.male,survival.died}418,{class.3rd,survival.died}528,{class.3rd,survival.survived}181,{sex.female}466,{sex.female,survival.died}127,{sex.female,survival.survived}339,{sex.male}843,{sex.male,survival.died}682,{sex.male,survival.survived}161,{survival.died}809,{survival.survived}500
Options  
(2)

SeeAlso
Apriori
RelatedGuides
▪
Association rule learning
""

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