itemsets-class | R Documentation |
The itemsets
class represents a set of itemsets and the associated
quality measures.
itemsets(items, itemLabels = NULL, quality = data.frame())
## S4 method for signature 'itemsets'
summary(object, ...)
## S4 method for signature 'itemsets'
length(x)
## S4 method for signature 'itemsets'
nitems(x)
## S4 method for signature 'itemsets'
labels(object, ...)
## S4 method for signature 'itemsets'
itemLabels(object)
## S4 replacement method for signature 'itemsets'
itemLabels(object) <- value
## S4 method for signature 'itemsets'
itemInfo(object)
## S4 method for signature 'itemsets'
items(x)
## S4 replacement method for signature 'itemsets'
items(x) <- value
## S4 method for signature 'itemsets'
tidLists(x)
items |
an itemMatrix or an object that can be converted using |
itemLabels |
item labels used for |
quality |
a data.frame with quality information (one row per itemset). |
object , x |
the object |
... |
further argments |
value |
replacement value |
Itemsets are usually created by calling an association rule mining algorithm
like apriori()
.
To create itemsets manually, the itemMatrix for the items of the itemsets
can be created using itemCoding. An example is in the Example
section below.
Mined itemsets sets contain several interest measures accessible
with the quality()
method. Additional measures can be
calculated via interestMeasure()
.
summary(itemsets)
: create a summary
length(itemsets)
: get the number of itemsets.
nitems(itemsets)
: get the number of items (columns) in the current encoding.
labels(itemsets)
: get the itemset labels.
itemLabels(itemsets)
: get the item labels.
itemLabels(itemsets) <- value
: replace the item labels.
itemInfo(itemsets)
: get item info data.frame.
items(itemsets)
: get items as an itemMatrix.
items(itemsets) <- value
: with a different itemMatrix.
tidLists(itemsets)
: get tidLists stored in the object (if any).
items
an itemMatrix object representing the itemsets.
tidLists
a tidLists or NULL
.
quality
a data.frame with quality information
info
a list with mining information.
Objects are the result of calling the
functions apriori()
(e.g., with target = "frequent itemsets"
in the parameter list) or eclat()
.
Objects can also
be created by calls of the form new("itemsets", ...)
or by using the constructor function
itemsets()
.
as("itemsets", "data.frame")
Michael Hahsler
Superclass: associations
Other associations functions:
abbreviate()
,
associations-class
,
c()
,
duplicated()
,
extract
,
inspect()
,
is.closed()
,
is.generator()
,
is.maximal()
,
is.redundant()
,
is.significant()
,
is.superset()
,
match()
,
rules-class
,
sample()
,
sets
,
size()
,
sort()
,
unique()
data("Adult")
## Mine frequent itemsets with Eclat.
fsets <- eclat(Adult, parameter = list(supp = 0.5))
## Display the 5 itemsets with the highest support.
fsets.top5 <- sort(fsets)[1:5]
inspect(fsets.top5)
## Get the itemsets as a list
as(items(fsets.top5), "list")
## Get the itemsets as a binary matrix
as(items(fsets.top5), "matrix")
## Get the itemsets as a sparse matrix, a ngCMatrix from package Matrix.
## Warning: for efficiency reasons, the ngCMatrix you get is transposed
as(items(fsets.top5), "ngCMatrix")
## Manually create itemsets with the item coding in the Adult dataset
## and calculate some interest measures
twoitemsets <- itemsets(
items = list(
c("age=Young", "relationship=Unmarried"),
c("age=Old")
), itemLabels = Adult)
quality(twoitemsets) <- data.frame(support = interestMeasure(twoitemsets,
measure = c("support"), transactions = Adult))
inspect(twoitemsets)
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