rules2matrix | R Documentation |
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.
rules2matrix(rules, measure = "support", reorder = "measure", ...) rules2groupedMatrix(rules, measure = "lift", measure2 = "support", k = 10, aggr.fun = mean, lhs_label_items = 2)
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 |
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. |
Michael Hahsler
Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3):317–335, May 2016. doi: 10.1007/s11573-016-0822-8.
plot
for rules using method = 'matrix'
and method = 'grouped matrix'
.
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)
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