Class itemsets — A Set of Itemsets
The itemsets class represents a set of itemsets and the associated
quality measures.
Itemsets are usually created by calling an association rule mining algorithm like apriori.
Itemsets store the items as an object of class itemMatrix.
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 typically contain several interest measures accessible with the quality method.
Additional measures can be calculated via interestMeasure.
items:object of class
itemMatrix containing the
items in the set of itemsets
quality:a data.frame containing the quality measures for the itemsets
tidLists:object of class
tidLists containing the IDs of the
transactions which support each itemset. The slot contains
NULL if no transactions ID list is available (transactions
ID lists are only available for eclat).
Class associations, directly.
signature(from = "itemsets", to =
"data.frame");
represent the itemsets in readable form
signature(x = "itemsets");
returns the itemMatrix
representing the set of itemsets
signature(x = "itemsets");
replaces the itemMatrix
representing the set of itemsets
signature(object = "itemsets");
returns the whole item information data frame including item
labels
signature(object = "itemsets");
returns labels for the itemsets as a
character vector. The labels have the following format:
"item1, item2,..., itemn"
signature(object = "itemsets");
returns the item labels used to encode the itemsets as
a character vector. The index for each label is the column
index of the item in the binary matrix.
signature(x = "itemsets"); number of all possible
items in the
binary matrix representation of the object.
signature(object = "itemsets")
signature(object = "itemsets");
returns the transaction ID list
Michael Hahsler
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")
## Create a rules for the Adult dataset manually and calcualte some interest Measures
twoitemsets <- new("itemsets",
items = encode(list(
c("age=Young", "relationship=Unmarried"),
c("age=Old")
), itemLabels = itemLabels(Adult))
)
quality(twoitemsets) <- data.frame(support = interestMeasure(twoitemsets,
measure = c("support"), transactions = Adult))
inspect(twoitemsets)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.