Association rule mining with modal logic
New building blocks
ModalAssociationRules.WorldMask
— Typeconst WorldMask = BitVector
Vector whose i-th position stores how many times a certain MeaningfulnessMeasure
applied on a specific Itemset
s is true on the i-th world of multiple instances.
If a single instance is considered, then this acts as a bit mask.
For example, if we consider 5 Kripke structures of a modal dataset, each of which containing 3 worlds, then the WorldMask
of an itemset could be [5,2,0], meaning that the itemset is always true on the first world of every instance. In the second world, the same itemset is true on it only for two instances. Considering the third world, then the itemset is never true.
See also Itemset
, MeaningfulnessMeasure
.
ModalAssociationRules.EnhancedItemset
— Typeconst EnhancedItemset = Tuple{Itemset,UInt32}
Compressed representation of multiple, identical Itemset
s.
See also Itemset
.
ModalAssociationRules.ConditionalPatternBase
— Typeconst ConditionalPatternBase = Vector{EnhancedItemset}
Collection of EnhancedItemset
.
This plays a central role in the state-of-the-art algorithm fpgrowth
, where a ConditionalPatternBase
embodies all the information needed to build an FPTree
data structure in the algorithm.
See also EnhancedItemset
, fpgrowth
, FPTree
.