fim4r | R Documentation |
Interfaces the algorithms implemented in fim4r. The algorithms include: Apriori, Eclat, FPgrowth, Carpenter, IsTa, RElim and SaM.
fim4r(
transactions,
method = NULL,
target = "frequent",
support = 0.1,
confidence = 0.8,
originalSupport = TRUE,
appear = NULL,
report = NULL,
verbose = TRUE,
...
)
transactions |
a transactions object |
method |
the algorithm to be used. One of:
|
target |
the target type. One of: |
support |
a numeric value for the minimal support in the range |
confidence |
a numeric value for the minimal confidence of rules in the range |
originalSupport |
logical; Use the support threshold on the support of the whole rule (LHS and RHS).
If |
appear |
Specify item appearance in rules (only for apriori, eclat, fpgrowth
and the target
|
report |
cannot be used via the interface. |
verbose |
logical; print used parameters? |
... |
further arguments are passed on to the function
the |
Installation:
The package fim4r is not available via CRAN. If needed,
the fim4r()
function downloads and installs the current version of the
package automatically. Your system needs to have build tools installed.
Build tools: You need to be able to install source packages. For Windows users this means that you need to install the RTools with a version matching your R version.
Additional Notes:
Support and confidence are specified here in the range [0,1]
.
This is different from the use in fim4r
package where supp
and conf
have the range [0, 100]
.
arules::fim4r()
automatically converts support and confidence internally.
fim4r
methods also return the empty itemset while arules
methods do not.
See ? fim4r::fim4r
for help on additional available arguments. This is only available
after package fim4r
is installed.
Algorithm descriptions and references can be found on the fim4r web page in the References Section.
An object of class itemsets or rules.
Christian Borgelt, fimi4r: Frequent Item Set Mining and Association Rule Induction for R. https://borgelt.net/fim4r.html
Other mining algorithms:
APappearance-class
,
AScontrol-classes
,
ASparameter-classes
,
apriori()
,
eclat()
,
ruleInduction()
,
weclat()
## Not run:
data(Adult)
# list available algorithms
fim4r()
# mine association rules with FPgrowth
r <- fim4r(Adult, method = "fpgrowth",
target = "rules", supp = .7, conf = .8)
r
inspect(head(r, by = "lift"))
# mine closed itemsets with Carpenter or IsTa
fim4r(Adult, method = "carpenter",
target = "closed", supp = .7)
fim4r(Adult, method = "ista",
target = "closed", supp = .7)
# mine frequent itemset of length 2 (zmin and zmax = 2)
freq_2 <- fim4r(Adult, method = "relim", target = "frequent", supp = .7,
zmin = 2, zmax = 2)
inspect(freq_2)
# mine maximal frequent itemsets
mfis <- fim4r(Adult, method = "sam", target = "maximal", supp = .7)
inspect(mfis)
# Examples for how to use item appearance with apriori, eclat,
# fpgrowth in fim4r. We first mine all rules.
inspect(fim4r(Adult, method = "fpgrowth",
target = "rules", supp = .8))
# ignore item "capital-gain=None"
inspect(fim4r(Adult, method = "fpgrowth",
target = "rules", supp = .8,
appear = list(c("capital-gain=None"), c("-"))))
# "capital-gain=None" cannot appear in consequent (antecedent only)
inspect(fim4r(Adult, method = "fpgrowth",
target = "rules", supp = .8,
appear = list(c("capital-gain=None"), c("a"))))
# "capital-gain=None" cannot appear in the antecedent
inspect(fim4r(Adult, method = "fpgrowth",
target = "rules", supp = .8,
appear = list(c("capital-gain=None"), c("c"))))
# restrict the consequent to the item "capital-gain=None".
# That is, "" = all items can only appear in the antecedent with the
# exception that "capital-gain=None" can only appear in the consequent.
inspect(fim4r(Adult, method = "fpgrowth",
target = "rules", supp = .8,
appear = list(c("", "capital-gain=None"), c("a", "c"))))
## End(Not run)
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