image: Visual Inspection of Binary Incidence Matrices

In arules: Mining Association Rules and Frequent Itemsets

Visual Inspection of Binary Incidence Matrices

Description

Provides image() methods to generate level plots to visually inspect binary incidence matrices, i.e., objects based on itemMatrix (e.g., transactions, tidLists, items in itemsets or rhs/lhs in rules). These plots can be used to identify problems in a data set (e.g., recording problems with some transactions containing all items).

Usage

## S4 method for signature 'itemMatrix'
image(x, xlab = "Items (Columns)", ylab = "Elements (Rows)", ...)

## S4 method for signature 'transactions'
image(x, xlab = "Items (Columns)", ylab = "Transactions (Rows)", ...)

## S4 method for signature 'tidLists'
image(x, xlab = "Transactions (Columns)", ylab = "Items/itemsets (Rows)", ...)

Arguments

x	the object (itemMatrix, transactions or tidLists).
xlab, ylab	labels for the plot.
...	further arguments passed on to image() in package Matrix which in turn are passed on to levelplot() in lattice.

Author(s)

Michael Hahsler

See Also

image() in package Matrix

Other itemMatrix and transactions functions: abbreviate(), c(), crossTable(), duplicated(), extract, hierarchy, inspect(), is.superset(), itemFrequency(), itemFrequencyPlot(), itemMatrix-class, match(), merge(), random.transactions(), sample(), sets, size(), supportingTransactions(), tidLists-class, transactions-class, unique()

Examples

data("Epub")

## in this data set we can see that not all
## items were available from the beginning.
image(Epub[1:1000])

arules documentation built on June 8, 2025, 12:10 p.m.