ASparameter-classes | R Documentation |
The ASparameter
class holds the mining parameters (e.g., minimum
support) for the used mining algorithms. APparameter
and
ECparameter
directly extend ASparameter
with additional slots
for parameters only suitable for apriori()
(APparameter
) or eclat()
(ECparameter
).
support
a numeric value for the
minimal support of an item set (default: 0.1
)
minlen
an integer value for the minimal number of items per item set (default: 1 item)
maxlen
an integer value for the maximal number of items per item set (default: 10 items)
target
a character string indicating the type of association mined. Partial names are matched. Available targets are:
"frequent itemsets"
"maximally frequent itemsets"
"generator frequent itemsets"
"closed frequent itemsets"
"rules"
only available for apriori;
use ruleInduction for eclat.
"hyperedgesets"
only available for apriori;
see references for the definition of association hyperedgesets.
ext
a logical indicating whether to report coverage (i.e., LHS-support)
as an extended quality measure (default: TRUE
)
confidence
a numeric value for the
minimal confidence of rules/association hyperedges (default:
0.8
). For frequent itemsets it is set to NA
.
smax
a numeric value for the maximal support of itemsets/rules/hyperedgesets (default: 1)
arem
a character string indicating the used additional rule
evaluation measure (default: "none"
) given by one of
"none"
: no additional evaluation measure
"diff"
: absolute confidence difference
"quot"
: difference of confidence quotient to 1
"aimp"
: absolute difference of improvement to 1
"info"
: information difference to prior
"chi2"
: normalized \chi^2
measure
Note: The measure is only reported if aval
is set to TRUE
.
Use minval
to set minimum thresholds on the measures.
aval
a logical indicating whether to
return the additional rule evaluation measure selected with arem
.
minval
a numeric value for the minimal value of additional
evaluation measure selected with arem
(default: 0.1
)
originalSupport
a logical indicating whether to
use the original definition of minimum support
(support of the LHS and RHS of the rule). If set to FALSE
then the support of the LHS (also called coverage of the rule) is returned as support.
The minimum support threshold is applied to this support. (default: TRUE
)
maxtime
Time limit in seconds for checking subsets.
maxtime = 0
disables the time limit. (default: 5 seconds)
tidLists
a logical indicating whether eclat()
should
return also a list of supporting transactions IDs.
(default: FALSE
)
APparameter
:
confidence
, minval
, smax
, arem
, aval
, originalSupport
, maxtime
, support
, minlen
, maxlen
, target
, ext
ECparameter
:
tidLists
, support
, minlen
, maxlen
, target
, ext
A suitable default parameter object will be
automatically created by apriori()
or
eclat()
. By specifying a named list (names equal to
slots) as parameter
argument for apriori()
or
eclat()
, the default values can be replaced with the values
in the list.
Objects can also be created via coercion.
as("NULL", "APparameter")
as("list", "APparameter")
as("NULL", "ECparameter")
as("list", "ECparameter")
Michael Hahsler and Bettina Gruen
Christian Borgelt (2004) Apriori — Finding Association Rules/Hyperedges with the Apriori Algorithm. https://borgelt.net/apriori.html
Other mining algorithms:
APappearance-class
,
AScontrol-classes
,
apriori()
,
eclat()
,
fim4r()
,
ruleInduction()
,
weclat()
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