All Classes

Class	Description
AbstractFrequentItemsetAlgorithm	Abstract base class for frequent itemset mining.
AbstractFrequentItemsetAlgorithm.Par	Parameterization class.
AddedValue	Added value (AV) interestingness measure: \( \text{confidence}(X \rightarrow Y) - \text{support}(Y) = P(Y|X)-P(Y) \).
APRIORI	The APRIORI algorithm for Mining Association Rules.
APRIORI.Par	Parameterization class.
AssociationRule	Association Rule
AssociationRuleGeneration	Association rule generation from frequent itemsets
AssociationRuleGeneration.ItemsetSearcher	Class to find itemsets in a sorted list.
AssociationRuleGeneration.Par	Parameterization class.
AssociationRuleGeneration.PartialItemset	Mutable scatch itemset for finding itemsets, based on SparseItemset.
AssociationRuleResult	Result class for association rule mining
CertaintyFactor	Certainty factor (CF; Loevinger) interestingness measure. \( \tfrac{\text{confidence}(X \rightarrow Y) - \text{support}(Y)}{\text{support}(\neg Y)} \).
Confidence	Confidence interestingness measure, \( \tfrac{\text{support}(X \cup Y)}{\text{support}(X)} = \tfrac{P(X \cap Y)}{P(X)}=P(Y|X) \).
Conviction	Conviction interestingness measure: \(\frac{P(X) P(\neg Y)}{P(X\cap\neg Y)}\).
Cosine	Cosine interestingness measure, \(\tfrac{\text{support}(A\cup B)}{\sqrt{\text{support}(A)\text{support}(B)}} =\tfrac{P(A\cap B)}{\sqrt{P(A)P(B)}}\).
DenseItemset	APRIORI itemset, dense representation.
Eclat	Eclat is a depth-first discovery algorithm for mining frequent itemsets.
Eclat.Par	Parameterization class.
FPGrowth	FP-Growth is an algorithm for mining the frequent itemsets by using a compressed representation of the database called FPGrowth.FPTree.
FPGrowth.FPNode	A single node of the FP tree.
FPGrowth.FPNode.Translator	Translator class for tree printing.
FPGrowth.FPTree	FP-Tree data structure
FPGrowth.FPTree.Collector	Interface for collecting frequent itemsets found.
FPGrowth.Par	Parameterization class.
FrequentItemsetsResult	Result class for frequent itemset mining algorithms.
GiniIndex	Gini-index based interestingness measure, using the weighted squared conditional probabilities compared to the non-conditional priors.
InterestingnessMeasure	Interface for interestingness measures.
Itemset	Frequent itemset.
Jaccard	Jaccard interestingness measure:
JMeasure	J-Measure interestingness measure.
Klosgen	Klösgen interestingness measure.
LaplaceCorrectedConfidence	Laplace Corrected Confidence interestingness measure.
Leverage	Leverage interestingness measure.
Lift	Lift interestingness measure.
OddsRatio	Odds ratio interestingness measure.
OneItemset	Frequent itemset with one element.
PhiCorrelationCoefficient	Phi Correlation Coefficient interestingness measure.
SebagSchonauer	Sebag Schonauer interestingness measure.
SmallDenseItemset	Frequent itemset, dense representation for up to 64 items.
SparseItemset	Frequent itemset, sparse representation.
YulesQ	Yule's Q interestingness measure.
YulesY	Yule's Y interestingness measure.