arulesViz (version 1.0-3)

plot: Plot method to visualize association rules and itemsets

Description

This is the S3 method to visualize association rules and itemsets. Implemented are several popular visualization methods including scatter plots with shading (two-key plots), graph based visualizations, doubledecker plots, etc.

Usage

## S3 method for class 'rules':
plot(x, method = NULL, measure = "support", shading = "lift", 
    interactive = FALSE, data = NULL, control = NULL, ...)
## S3 method for class 'itemsets':
plot(x, method = NULL, measure = "support", shading = NA,
    interactive=FALSE, data = NULL, control = NULL, ...)

Arguments

x

an object of class "rules" or "itemsets".

method

a string with value "scatterplot", "two-key plot", "matrix", "matrix3D", "mosaic", "doubledecker", "graph", "paracoord" or "grouped", "iplots" selecting the visualization method (see Details).

measure

measure(s) of interestingness (e.g., "support", "confidence", "lift", "order") used in the visualization. Some visualization methods need one measure, others take a vector with two measures (e.g., scatterplot). In some plots (e.g., graphs)

shading

measure of interestingness used for the color of the points/arrows/nodes (e.g., "support", "confidence", "lift"). The default is "lift". NA can be often used to suppress shading.

interactive

enable interactive exploration (not implemented by all methods).

control

a list of control parameters for the plot. The available control parameters depends on the visualization technique (see Details).

data

the dataset (class "transactions") used to generate the rules/itemsets. Only "mosaic" and "doubledecker" require the original data.

...

further arguments are typically passed on to the used low-level plotting function.

Value

• Several interactive plots return a set of selected rules/itemsets. Other plots might return other data structures. For example graph-based plots return the graph (invisibly).

Details

Most visualization techniques are described by Bruzzese and Davino (2008), however, we added more color shading, reordering and interactive features. The following visualization method are available:

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

Bruzzese, D. and Davino, C. (2008), Visual Mining of Association Rules, in Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, Springer-Verlag, pp. 103--122.

Hahsler M. and Chelluboina S. (2011), Visualizing association rules in hierarchical groups. In 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms (Interface 2011). The Interface Foundation of North America.

See Also

scatterplot3d in scatterplot3d, plot.igraph and tkplot in igraph, seriate in seriation

Examples

data(Groceries)
rules <- apriori(Groceries, parameter=list(support=0.005, confidence=0.5))
rules

## Scatterplot
plot(rules)
## try: sel <- plot(rules, interactive=TRUE)

## Scatterplot with custom colors
library(colorspace) # for sequential_hcl
plot(rules, control=list(col=sequential_hcl(100)))
    
## Two-key plot is a scatterplot with shading = "order"
plot(rules, shading="order", control=list(main = "Two-key plot", 
  col=rainbow(5)))

## The following techniques work better with fewer rules
subrules <- subset(rules, lift>2.5)
subrules
  
## 2D matrix with shading
plot(subrules, method="matrix", measure="lift")
plot(subrules, method="matrix", measure="lift", control=list(reorder=TRUE))

## 3D matrix
plot(subrules, method="matrix3D", measure="lift")
plot(subrules, method="matrix3D", measure="lift", control=list(reorder=TRUE))

## matrix with two measures
plot(subrules, method="matrix", measure=c("lift", "confidence"))
plot(subrules, method="matrix", measure=c("lift", "confidence"), 
	control=list(reorder=TRUE))

## try: plot(subrules, method="matrix", measure="lift", interactive=TRUE, 
##		control=list(reorder=TRUE))

## grouped matrix plot
plot(rules, method="grouped")
## try: sel <- plot(rules, method="grouped", interactive=TRUE)

## graphs only work well with very few rules
subrules2 <- sample(rules, 10)
plot(subrules2, method="graph")
## igraph layout generators can be used (see ? igraph::layout_)
plot(subrules2, method="graph", control=list(layout=igraph::in_circle()))
plot(subrules2, method="graph", control=list(
  layout=igraph::with_graphopt(spring.const=5, mass=50)))

plot(subrules2, method="graph", control=list(type="itemsets"))
## try: plot(subrules2, method="graph", interactive=TRUE)
## try: plot(subrules2, method="graph", control=list(engine="graphviz"))


## parallel coordinates plot
plot(subrules2, method="paracoord")
plot(subrules2, method="paracoord", control=list(reorder=TRUE))

## Doubledecker plot only works for a single rule
oneRule <- sample(rules, 1)
plot(oneRule, method="doubledecker", data = Groceries)

## use iplots (experimental)
## try: sel <- plot(rules, method="iplots", interactive=TRUE)


## for itemsets
itemsets <- eclat(Groceries, parameter = list(support = 0.02, minlen=2))
plot(itemsets)
plot(itemsets, method="graph")
plot(itemsets, method="paracoord", control=list(alpha=.5, reorder=TRUE))

## add more quality measures to use for the scatterplot
quality(itemsets) <- interestMeasure(itemsets, trans=Groceries)
head(quality(itemsets))
plot(itemsets, measure=c("support", "allConfidence"), shading="lift")