plot: Plot method to visualize association rules and itemsets
In arulesViz: Visualizing Association Rules and Frequent Itemsets
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
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
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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) NA
can be used to suppress using a measure.
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.
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:
- "scatterplot", "two-key plot"
-
This visualization method draws a two dimensional scatterplot with different
measures of interestingness (parameter "measure") on the axes and a third
measure (parameter "shading") is represented by the color of the points.
There is a special value for shading called "order" which produces a
two-key plot where the color of the points represents the length (order)
of the rule.
The list of control parameters for this method is
- "main"
plot title
- "pch"
use filled symbols: 20–25
- "cex"
symbol size
- "xlim","ylim"
limits
- "jitter"
a number greater than 0 adds jitter to the points
- "col"
color palette (default is 100 gray values.)
Interactive manipulations are available.
- "matrix", "matrix3D"
-
Arranges the association rules as a matrix with the itemsets in the antecedents
on one axis and the itemsets in the consequent on the other. The interest
measure is either visualized by a color (darker means a higher value for the
measure) or as the height of a bar (method "matrix3D").
The list of control parameters for this method is
- "main"
plot title
- "type"
defines the way the data is rendered: "grid", "image"
or "3D" (scatterplot3d)
- "reorder"
if TRUE then the itemsets on the x and y-axes
are reordered to bring rules with similar values for the interest measure
closer together and make the plot clearer.
- "orderBy"
specifies the measure of
interest for reordering (default is the visualized measure)
- "reorderMethod","reorderControl","reorderDist"
seriation method, control arguments and distance method (default "euclidean")
used for reordering (see seriate() method in seriation)
- "col"
a vector of n colors used for the plot (default: 100 gray values)
- "xlim","ylim"
limits
Currently there is no interactive version available.
- "grouped"
-
Grouped matrix-based visualization (Hahsler and Chelloboina, 2011).
Antecedents (columns) in the matrix are
grouped using clustering. Groups are represented as balloons
in the matrix.
The list of control parameters for this method is
- "main"
plot title
- "k"
number of antecedent groups (default: 20)
- "aggr.fun"
aggregation function
can be any function computing a scalar from a vector
(e.g., min, mean, median (default), sum, max). It is also used
to reorder the balloons in the plot.
- "col"
color palette (default is 100 gray values.)
Interactive manipulations are available.
- "graph"
-
Represents the rules (or itemsets) as a graph.
Control arguments are
- "main"
plot title
- "cex"
cex for labels
- "itemLabels"
display item/itemset names instead of ids (TRUE)
- "measureLabels"
display values of interest measures (FALSE)
- "precision"
number of digits for numbers in plot.
- "type"
vertices represent: "items" (default) or "itemsets"
- "engine"
graph layout engine: "igraph" (default) or "graphviz"
- "layout"
layout algorithm defined in igraph or Rgraphviz
(default: igraph::nicely which usually does a Fruchterman-Reingold layout
for engine igraph and "dot"/"neato" for graphviz)
- "arrowSize"
[0,1]
- "alpha"
alpha transparency value (default .8; set to 1 for no transparency)
For the igraph engine the used plot function is plot.igraph
in igraph.
For graphviz the function plot in
Rgraphviz is used. Note that Rgraphviz is available at
http://www.bioconductor.org/.
For the
interactive version tkplot in igraph is always used.
... arguments are passed on to the respective plotting function (use for color, etc.).
- "doubledecker", "mosaic"
-
Represents a single rule as a doubledecker or mosaic plot.
Parameter data has to be specified to compute the needed contingency
table.
Available control parameters are
- "main"
plot title
- "paracoord"
-
Represents the rules (or itemsets) as a parallel coordinate plot.
Available control parameters are
- "main"
plot title
- "reorder"
reorder to minimize crossing lines.
- "alpha"
alpha transparency value
Currently there is no interactive version available.
- "iplots"
-
Experimental interactive plots (package iplots)
which support selection, highlighting,
brushing, etc. Currently plots a scatterplot (support vs. confidence) and
several histograms. Interactive manipulations are available.
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).
Author(s)
Michael Hahsler and Sudheer Chelluboina. Some visualizations are based on
the implementation by Martin Vodenicharov.
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
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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")
Example output
Loading required package: arules
Loading required package: Matrix
Attaching package: 'arules'
The following objects are masked from 'package:base':
abbreviate, write
Loading required package: grid
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.5 0.1 1 none FALSE TRUE 5 0.005 1
maxlen target ext
10 rules FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 49
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
sorting and recoding items ... [120 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [120 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
set of 120 rules
set of 41 rules
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Warning message:
In plot.rules(subrules, method = "matrix3D", measure = "lift") :
method 'matrix3D' is deprecated use method 'matrix' with engine '3d'
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Warning message:
In plot.rules(subrules, method = "matrix3D", measure = "lift", control = list(reorder = TRUE)) :
method 'matrix3D' is deprecated use method 'matrix' with engine '3d'
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Warning: Unknown control parameters: type
Available control parameters (with default values):
main = Graph for 10 rules
nodeColors = c("#66CC6680", "#9999CC80")
nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha = 0.5
cex = 1
itemLabels = TRUE
labelCol = #000000B3
measureLabels = FALSE
precision = 3
layout = NULL
layoutParams = list()
arrowSize = 0.5
engine = igraph
plot = TRUE
plot_options = list()
max = 100
verbose = FALSE
Eclat
parameter specification:
tidLists support minlen maxlen target ext
FALSE 0.02 2 10 frequent itemsets FALSE
algorithmic control:
sparse sort verbose
7 -2 TRUE
Absolute minimum support count: 196
create itemset ...
set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
sorting and recoding items ... [59 item(s)] done [0.00s].
creating sparse bit matrix ... [59 row(s), 9835 column(s)] done [0.00s].
writing ... [63 set(s)] done [0.01s].
Creating S4 object ... done [0.00s].
support allConfidence crossSupportRatio lift
1 0.02043721 0.07998408 0.1882212 1.663094
2 0.02125064 0.08316753 0.2053323 1.585180
3 0.02613116 0.10226821 0.2085157 1.919481
4 0.02216573 0.08674891 0.2256267 1.504719
5 0.02165735 0.11192853 0.2979506 1.941476
6 0.02053889 0.08038201 0.2307998 1.363029
Warning message:
system call failed: Cannot allocate memory
arulesViz documentation built on May 2, 2019, 6:50 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) References See Also Examples
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
1 2 3 4 5 6 |
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".
|
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. |
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:
- "scatterplot", "two-key plot"
-
This visualization method draws a two dimensional scatterplot with different measures of interestingness (parameter "measure") on the axes and a third measure (parameter "shading") is represented by the color of the points. There is a special value for shading called "order" which produces a two-key plot where the color of the points represents the length (order) of the rule.
The list of control parameters for this method is
- "main"
plot title
- "pch"
use filled symbols: 20–25
- "cex"
symbol size
- "xlim","ylim"
limits
- "jitter"
a number greater than 0 adds jitter to the points
- "col"
color palette (default is 100 gray values.)
Interactive manipulations are available.
- "matrix", "matrix3D"
-
Arranges the association rules as a matrix with the itemsets in the antecedents on one axis and the itemsets in the consequent on the other. The interest measure is either visualized by a color (darker means a higher value for the measure) or as the height of a bar (method "matrix3D").
The list of control parameters for this method is
- "main"
plot title
- "type"
defines the way the data is rendered: "grid", "image" or "3D" (scatterplot3d)
- "reorder"
if
TRUEthen the itemsets on the x and y-axes are reordered to bring rules with similar values for the interest measure closer together and make the plot clearer.- "orderBy"
specifies the measure of interest for reordering (default is the visualized measure)
- "reorderMethod","reorderControl","reorderDist"
seriation method, control arguments and distance method (default "euclidean") used for reordering (see
seriate()method in seriation)- "col"
a vector of n colors used for the plot (default: 100 gray values)
- "xlim","ylim"
limits
Currently there is no interactive version available.
- "grouped"
-
Grouped matrix-based visualization (Hahsler and Chelloboina, 2011). Antecedents (columns) in the matrix are grouped using clustering. Groups are represented as balloons in the matrix.
The list of control parameters for this method is
- "main"
plot title
- "k"
number of antecedent groups (default: 20)
- "aggr.fun"
aggregation function can be any function computing a scalar from a vector (e.g., min, mean, median (default), sum, max). It is also used to reorder the balloons in the plot.
- "col"
color palette (default is 100 gray values.)
Interactive manipulations are available.
- "graph"
-
Represents the rules (or itemsets) as a graph.
Control arguments are
- "main"
plot title
- "cex"
cex for labels
- "itemLabels"
display item/itemset names instead of ids (
TRUE)- "measureLabels"
display values of interest measures (
FALSE)- "precision"
number of digits for numbers in plot.
- "type"
vertices represent: "items" (default) or "itemsets"
- "engine"
graph layout engine: "igraph" (default) or "graphviz"
- "layout"
layout algorithm defined in igraph or Rgraphviz (default: igraph::nicely which usually does a Fruchterman-Reingold layout for engine igraph and "dot"/"neato" for graphviz)
- "arrowSize"
[0,1]
- "alpha"
alpha transparency value (default .8; set to 1 for no transparency)
For the igraph engine the used plot function is
plot.igraphin igraph. For graphviz the functionplotin Rgraphviz is used. Note that Rgraphviz is available at http://www.bioconductor.org/. For the interactive versiontkplotin igraph is always used....arguments are passed on to the respective plotting function (use for color, etc.). - "doubledecker", "mosaic"
-
Represents a single rule as a doubledecker or mosaic plot. Parameter data has to be specified to compute the needed contingency table. Available control parameters are
- "main"
plot title
- "paracoord"
-
Represents the rules (or itemsets) as a parallel coordinate plot. Available control parameters are
- "main"
plot title
- "reorder"
reorder to minimize crossing lines.
- "alpha"
alpha transparency value
Currently there is no interactive version available.
- "iplots"
-
Experimental interactive plots (package iplots) which support selection, highlighting, brushing, etc. Currently plots a scatterplot (support vs. confidence) and several histograms. Interactive manipulations are available.
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).
Author(s)
Michael Hahsler and Sudheer Chelluboina. Some visualizations are based on the implementation by Martin Vodenicharov.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | 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")
|
Example output
Loading required package: arules
Loading required package: Matrix
Attaching package: 'arules'
The following objects are masked from 'package:base':
abbreviate, write
Loading required package: grid
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.5 0.1 1 none FALSE TRUE 5 0.005 1
maxlen target ext
10 rules FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 49
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
sorting and recoding items ... [120 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [120 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
set of 120 rules
set of 41 rules
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Warning message:
In plot.rules(subrules, method = "matrix3D", measure = "lift") :
method 'matrix3D' is deprecated use method 'matrix' with engine '3d'
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Warning message:
In plot.rules(subrules, method = "matrix3D", measure = "lift", control = list(reorder = TRUE)) :
method 'matrix3D' is deprecated use method 'matrix' with engine '3d'
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Itemsets in Antecedent (LHS)
[1] "{citrus fruit,root vegetables,whole milk}"
[2] "{tropical fruit,curd}"
[3] "{pip fruit,root vegetables,whole milk}"
[4] "{root vegetables,onions}"
[5] "{citrus fruit,root vegetables}"
[6] "{tropical fruit,root vegetables,whole milk}"
[7] "{tropical fruit,root vegetables}"
[8] "{tropical fruit,whipped/sour cream}"
[9] "{tropical fruit,butter}"
[10] "{root vegetables,fruit/vegetable juice}"
[11] "{root vegetables,whole milk,whipped/sour cream}"
[12] "{pip fruit,whipped/sour cream}"
[13] "{onions,whole milk}"
[14] "{root vegetables,whole milk,yogurt}"
[15] "{whole milk,yogurt,fruit/vegetable juice}"
[16] "{root vegetables,pastry}"
[17] "{butter,whipped/sour cream}"
[18] "{root vegetables,margarine}"
[19] "{pip fruit,whole milk,yogurt}"
[20] "{tropical fruit,root vegetables,yogurt}"
[21] "{root vegetables,frozen vegetables}"
[22] "{chicken,root vegetables}"
[23] "{citrus fruit,whipped/sour cream}"
[24] "{pip fruit,root vegetables}"
[25] "{root vegetables,newspapers}"
[26] "{root vegetables,shopping bags}"
[27] "{pork,root vegetables}"
[28] "{whole milk,yogurt,whipped/sour cream}"
[29] "{root vegetables,butter}"
[30] "{pip fruit,root vegetables,other vegetables}"
[31] "{root vegetables,domestic eggs}"
[32] "{whipped/sour cream,domestic eggs}"
[33] "{root vegetables,curd}"
[34] "{tropical fruit,whole milk,yogurt}"
[35] "{root vegetables,rolls/buns}"
[36] "{root vegetables,whipped/sour cream}"
[37] "{root vegetables,yogurt}"
[38] "{butter,yogurt}"
Itemsets in Consequent (RHS)
[1] "{whole milk}" "{other vegetables}" "{yogurt}"
Warning: Unknown control parameters: type
Available control parameters (with default values):
main = Graph for 10 rules
nodeColors = c("#66CC6680", "#9999CC80")
nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha = 0.5
cex = 1
itemLabels = TRUE
labelCol = #000000B3
measureLabels = FALSE
precision = 3
layout = NULL
layoutParams = list()
arrowSize = 0.5
engine = igraph
plot = TRUE
plot_options = list()
max = 100
verbose = FALSE
Eclat
parameter specification:
tidLists support minlen maxlen target ext
FALSE 0.02 2 10 frequent itemsets FALSE
algorithmic control:
sparse sort verbose
7 -2 TRUE
Absolute minimum support count: 196
create itemset ...
set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
sorting and recoding items ... [59 item(s)] done [0.00s].
creating sparse bit matrix ... [59 row(s), 9835 column(s)] done [0.00s].
writing ... [63 set(s)] done [0.01s].
Creating S4 object ... done [0.00s].
support allConfidence crossSupportRatio lift
1 0.02043721 0.07998408 0.1882212 1.663094
2 0.02125064 0.08316753 0.2053323 1.585180
3 0.02613116 0.10226821 0.2085157 1.919481
4 0.02216573 0.08674891 0.2256267 1.504719
5 0.02165735 0.11192853 0.2979506 1.941476
6 0.02053889 0.08038201 0.2307998 1.363029
Warning message:
system call failed: Cannot allocate memory
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