arules (version 1.5-0)

itemMatrix-class: Class ``itemMatrix'' --- Sparse Binary Incidence Matrix to Represent Sets of Items

Description

The itemMatrix class is the basic building block for transactions, itemsets and rules in package arules. The class contains a sparse Matrix representation of items (a set of itemsets or transactions) and the corresponding item labels.

Objects from the Class

Objects can be created by calls of the form new("itemMatrix", ...). However, most of the time objects will be created by coercion from a matrix, list or data.frame.

Slots

Methods

Details

Sets of itemsets are represented as sparse binary matrices. If you work with several itemMatrices at the same time (e.g., several transaction sets, lhs and rhs of a rule, etc.), then the encoding (itemLabes and order of the items in the binary matrix) in the different itemMatrices is important and needs to conform. See itemCoding to learn how to encode and recode itemMatrix objects.

See Also

LIST, c, duplicated, inspect, is.subset, is.superset, itemFrequency, itemFrequencyPlot, itemCoding, match, length, sets, subset, unique, [-methods, image, ngCMatrix-class (from Matrix), transactions-class, itemsets-class, rules-class

Examples

set.seed(1234)
  
## Generate random data and coerce data to itemMatrix.
m <- matrix(runif(100000)>0.8, ncol=20)
dimnames(m) <- list(NULL, paste("item", c(1:20), sep=""))
i <- as(m, "itemMatrix")

## Get the number of elements (rows) in the itemMatrix.
length(i)

## Get first 5 elements (rows) of the itemMatrix as list.
as(i[1:5], "list")

## Get first 5 elements (rows) of the itemMatrix as matrix.
as(i[1:5], "matrix")

## Get first 5 elements (rows) of the itemMatrix as sparse ngCMatrix.
## Warning: for efficiency reasons, the ngCMatrix you get is transposed!
as(i[1:5], "ngCMatrix")

## Get labels for the first 5 itemsets (first default and then with 
## custom formating)
labels(i[1:5])
labels(i[1:5], itemSep = " + ", setStart = "", setEnd = "")

## create itemsets from itemMatrix  
is <- new("itemsets", items = i[1:3])
inspect(is)

## create rules (rhs and lhs cannot share items so I use 
## itemSetdiff here). Also assign (random) support.
rules <- new("rules", lhs=itemSetdiff(i[4:6],i[1:3]), rhs=i[1:3],
  quality = data.frame(support = runif(3)))
inspect(rules)