rules2matrix: Convert association rules into a matrix

In arulesViz: Visualizing Association Rules and Frequent Itemsets

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

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

rules2matrix(rules, measure = "support", reorder = "measure", ...)

Arguments

rules	a rules object.
measure	quality measure put in the matrix
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).
reorder	reorder rows and columns? Possible methods are: "none", "measure" (default), "support/confidence", "similarity".
...	passed on to arules::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.

Author(s)

Michael Hahsler

References

Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3):317–335, May 2016. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11573-016-0822-8")}.

See Also

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

Examples

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
gm <- rules2groupedMatrix(rules, k = 10)
gm$m

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

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

# create the corresponding grouped matrix plot by passing the grouped matrix as the groups parameter
plot(rules, method = "grouped matrix", groups = gm)

arulesViz documentation built on May 29, 2024, 4:37 a.m.