The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
One of the goals in the design of this package is to be able to
integrate with the arules
package. This means that any one
using the arules
functionalities can export to and import
from fcaR
objects, more precisely,
FormalContext
and ImplicationSet
objects.
library(arules)
#> Loading required package: Matrix
#>
#> Attaching package: 'arules'
#> The following objects are masked from 'package:base':
#>
#> abbreviate, write
library(fcaR)
#>
#> Attaching package: 'fcaR'
#> The following object is masked from 'package:Matrix':
#>
#> %&%
For these examples, we are using two binary datasets,
Mushroom
(from the arules
package) and
planets
(from fcaR
).
data("Mushroom", package = "arules")
At the moment, in arules
there is no support for fuzzy
sets, so we must restrict ourselves to the binary case.
Let us create a FormalContext
object for the
planets
dataset:
<- FormalContext$new(planets) fc_planets
We begin by converting between the objects which store the datasets.
It suffices to initialize a FormalContext
object with
the transactions dataset:
<- FormalContext$new(Mushroom)
fc
fc#> FormalContext with 8124 objects and 114 attributes.
#> Class=edible Class=poisonous CapShape=bell CapShape=conical CapShape=flat
#> 1 X
#> 2 X
#> 3 X X
#> 4 X
#> 5 X
#> 6 X
#> 7 X X
#> 8 X X
#> 9 X
#> 10 X X
#> Other attributes are: CapShape=knobbed, CapShape=sunken, CapShape=convex,
#> CapSurf=fibrous, CapSurf=grooves, CapSurf=smooth, ...
From this point, we can use all the functionalities in the
fcaR
package regarding formal contexts, concept lattices
and implication sets.
The to_transactions()
function enables us to export a
formal context to a format compatible with the arules
package:
$to_transactions()
fc_planets#> transactions in sparse format with
#> 9 transactions (rows) and
#> 7 items (columns)
and use the functionality in that package.
Other point of integration between the two packages is the ability to
import rules from the arules
package, operate on them to
compute closures, recommendations or to remove redundancies, or to
export an implication set as a rules
object.
Let us suppose that we have extracted implications from the
Mushroom
dataset using the apriori()
function:
<- apriori(Mushroom, parameter = list(conf = 1), control = list(verbose = FALSE)) mushroom_rules
Once we have created the fc
object storing the
Mushroom
dataset, we simply add the implications to it
as:
$implications$add(mushroom_rules) fc
And we can use all the functionalities for the
ImplicationSet
class.
If we want to export the implications extracted for a binary formal context, we can use:
$find_implications()
fc_planets$implications$to_arules(quality = TRUE)
fc_planets#> set of 10 rules
An example of use may be to extract rules in the arules
package by using apriori()
or eclat()
, then
importing everything into fcaR
as described above, and use
the functionalities to simplify, remove redundancies, compute closures,
etc., as needed, and then re-export back to arules
.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.