| AScontrol-classes {arules} | R Documentation |
Classes AScontrol, APcontrol, ECcontrol — Specifying the control Argument of Apriori and Eclat
Description
The AScontrol class holds the algorithmic parameters for the used
mining algorithms. APcontrol and ECcontrol directly extend
AScontrol with additional slots for parameters only suitable for the
algorithms Apriori (APcontrol) and Eclat (ECcontrol).
Slots
sortan integer scalar indicating how to sort items with respect to their frequency: (default: 2)
1: ascending
-1: descending
0: do not sort
2: ascending
-2: descending with respect to transaction size sum
verbosea logical indicating whether to report progress
filtera numeric scalar indicating how to filter unused items from transactions (default: 0.1)
-
=0: do not filter items with respect to. usage in sets -
<0: fraction of removed items for filtering -
>0: take execution times ratio into account
-
treea logical indicating whether to organize transactions as a prefix tree (default:
TRUE)heapa logical indicating whether to use heapsort instead of quicksort to sort the transactions (default:
TRUE)memopta logical indicating whether to minimize memory usage instead of maximize speed (default:
FALSE)loada logical indicating whether to load transactions into memory (default:
TRUE)sparsea numeric value for the threshold for sparse representation (default: 7)
Available Slots by Subclass
-
APcontrol:filter,tree,heap,memopt,load,sort,verbose -
ECcontrol:sparse,sort,verbose
Objects from the Class
A suitable default control object will be
automatically created by the apriori() or the
eclat() function. By specifying a named list (names equal to
slots) as the control argument for apriori() or
eclat(), default values can be replaced with the values
in the list.
Objects can also be created via coercion.
Coercions
-
as("NULL", "APcontrol") -
as("list", "APcontrol") -
as("NULL", "ECcontrol") -
as("list", "ECcontrol")
Author(s)
Michael Hahsler and Bettina Gruen
References
Christian Borgelt (2004) Apriori — Finding Association Rules/Hyperedges with the Apriori Algorithm. https://borgelt.net/apriori.html
See Also
Other mining algorithms:
APappearance-class,
ASparameter-classes,
apriori(),
eclat(),
fim4r(),
ruleInduction(),
weclat()