arulesViz (version 1.5-0)

rules2matrix: Convert association rules into a matrix

Description

Converts a set of association rules into a matrix with unique LHS itemsets as columns and unique RHS itemsets as rows. The matrix cells contain a quality measure. The LHS itemsets can be grouped.

Usage

rules2matrix(rules, measure = "support", reorder = "measure", ...)
rules2groupedMatrix(rules, measure = "lift", measure2 = "support", k = 10, 
  aggr.fun = mean, lhs_label_items = 2)

Arguments

rules

a rules object.

measure

quality measure put in the matrix

reorder

reorder rows and columns? Possible methods are: "none", "measure" (default), "support/confidence", "similarity".

measure2

second quality measure (organized in the same way as measure).

k

number of LHS itemset groups.

aggr.fun

function to aggregate the quality measure for groups.

lhs_label_items

number of top items used to name LHS itemset groups (columns).

...

passed on to DATAFRAME.

Value

rules2matrix returns a matrix with quality values.

rules2groupedMatrix returns a list with elements

m

the grouped matrix for measure.

m2

the grouped matrix for measure2.

clustering_rules

vector with group assignment for each rule.

References

Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3):317--335, May 2016. 10.1007/s11573-016-0822-8.

See Also

plot for rules using method = 'matrix' and method = 'grouped matrix'.

Examples

# NOT RUN {
data(Groceries)
rules <- apriori(Groceries, parameter=list(support = 0.001, confidence = 0.8))
rules

## Matrix
m <- rules2matrix(rules[1:10], measure = "lift")
m
plot(rules[1:10], method = "matrix")

## Grouped matrix
# create a matrix with LHSs grouped in k = 10 groups
m <- rules2groupedMatrix(rules, k = 10)
m$m

# number of rules per group 
table(m$clustering_rules)

# get rules for group 1
inspect(rules[m$clustering_rules == 1])

# the corresponding plot
plot(rules, method = "grouped matrix", k = 10)
# }