AprioriMiner

java.lang.Object
- net.sf.tweety.machinelearning.assoc.AbstractAssociationRuleMiner<T>
- - net.sf.tweety.machinelearning.assoc.AprioriMiner<T>

Type Parameters:

T - the type of items

All Implemented Interfaces:

AssociationRuleMiner<T>
```
public class AprioriMiner<T>
extends AbstractAssociationRuleMiner<T>
```
Implements the classical Apriori algorithm for association rule mining, cf. [R. Agrawal, R. Srikant. Fast algorithms for mining association rules in large databases. VLDB 1994.]

Author:

Matthias Thimm

Constructor Summary

Constructors
Constructor	Description
`AprioriMiner(double minsupport, double minconf)`	Creates a new Apriori miner with the given minimum support and minimum confidence values.

Method Summary

Modifier and Type	Method	Description
`java.util.Collection<AssociationRule<T>>`	`mineRules(java.util.Collection<java.util.Collection<T>> database, int conclusion_limit, int total_limit)`	Mines a set of association rules from the given database.

Methods inherited from class net.sf.tweety.machinelearning.assoc.AbstractAssociationRuleMiner
mineRules, mineRules

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - AprioriMiner
```
public AprioriMiner(double minsupport,
                    double minconf)
```
    Creates a new Apriori miner with the given minimum support and minimum confidence values.
    
    Parameters:
    
    minsupport - the minimum confidence for mined rules.
    
    minconf - the minimum support for mined rules.
- Method Detail
  - mineRules
```
public java.util.Collection<AssociationRule<T>> mineRules(java.util.Collection<java.util.Collection<T>> database,
                                                          int conclusion_limit,
                                                          int total_limit)
```
    Description copied from interface: AssociationRuleMiner
    
    Mines a set of association rules from the given database. Only those rules are mined where the conclusion and in total has the maximal given number of elements, respectively.
    
    Parameters:
    
    database - some database
    
    conclusion_limit - the maximal size of elements in the conclusion of the mined rules.
    
    total_limit - the total number of elements that may appear in the mined rules.
    
    Returns:
    
    a set of association rules.