arules (version 1.5-0)

transactions-class: Class ``transactions'' --- Binary Incidence Matrix for Transactions

Description

The transactions class represents transaction data used for mining itemsets or rules. It is a direct extension of class itemMatrix to store a binary incidence matrix, item labels, and optionally transaction IDs and user IDs.

Objects from the Class

Objects are created by coercion from objects of other classes (see Examples section) or by calls of the form new("transactions", ...).

Slots

Extends

Class itemMatrix, directly.

Methods

Details

Transactions can be created by coercion from lists containing transactions, but also from matrix and data.frames. However, you will need to prepare your data first. Association rule mining can only use items and does not work with continuous variables.

For example, an item describing a person (i.e., the considered object called a transaction) could be tall. The fact that the person is tall would be encoded in the transaction containing the item tall. This is typically encoded in a transaction-by-items matrix by a TRUE value. This is why as.transaction can deal with logical columns, because it assumes the column stands for an item. The function also can convert columns with nominal values (i.e., factors) into a series of binary items (one for each level). So if you have nominal variables then you need to make sure they are factors (and not characters or numbers) using something like

data[,"a_nominal_var"] <- factor(data[,"a_nominal_var"]).

Continuous variables need to be discretized first. An item resulting from discretization might be age>18 and the column contains only TRUE or FALSE. Alternatively it can be a factor with levels age<=18< em="">, 50=>age>18 and age>50. These will be automatically converted into 3 items, one for each level. Have a look at the function discretize for automatic discretization.

Complete examples for how to prepare data can be found in the man pages for Income and Adult.

Transactions are represented as sparse binary matrices of class itemMatrix. If you work with several transaction sets at the same time, then the encoding (order of the items in the binary matrix) in the different sets is important. See itemCoding to learn how to encode and recode transaction sets.

See Also

[-methods, discretize, LIST, write, c, image, inspect, itemCoding, read.transactions, random.transactions, sets, itemMatrix-class

Examples

## example 1: creating transactions form a list
a_list <- list(
      c("a","b","c"),
      c("a","b"),
      c("a","b","d"),
      c("c","e"),
      c("a","b","d","e")
      )

## set transaction names
names(a_list) <- paste("Tr",c(1:5), sep = "")
a_list

## coerce into transactions
trans1 <- as(a_list, "transactions")

## analyze transactions
summary(trans1)
image(trans1)

## example 2: creating transactions from a matrix
a_matrix <- matrix(c(
  1,1,1,0,0,
	1,1,0,0,0,
	1,1,0,1,0,
	0,0,1,0,1,
	1,1,0,1,1
  ), ncol = 5)

## set dim names
dimnames(a_matrix) <- list(c("a","b","c","d","e"),
	paste("Tr",c(1:5), sep = ""))

a_matrix

## coerce
trans2 <- as(a_matrix, "transactions")
trans2
inspect(trans2)

## example 3: creating transactions from data.frame
a_df <- data.frame(
	age   = as.factor(c(6, 8, NA, 9, 16)), 
	grade = as.factor(c("A", "C", "F", NA, "C")),
  pass  = c(TRUE, TRUE, FALSE, TRUE, TRUE))  
## note: factors are translated differently to logicals and NAs are ignored
a_df

## coerce
trans3 <- as(a_df, "transactions") 
inspect(trans3)
as(trans3, "data.frame")

## example 4: creating transactions from a data.frame with 
## transaction IDs and items 
a_df3 <- data.frame(
  TID = c(1,1,2,2,2,3), 
  item=c("a","b","a","b","c", "b")
  )
a_df3
trans4 <- as(split(a_df3[,"item"], a_df3[,"TID"]), "transactions")
trans4
inspect(trans4)