nuggets (version 2.1.0)

cluster_associations: Cluster association rules

Description

This function clusters association rules based on the selected numeric attribute by (e.g., confidence or lift) and summarizes the clusters. The clustering is performed using the k-means algorithm.

Usage

cluster_associations(
  x,
  n,
  by,
  algorithm = "Hartigan-Wong",
  predicates_in_label = 2
)

Value

A tibble with one row per cluster. The columns are:

• cluster: the cluster number;

• cluster_label: a label for the cluster, consisting of the number of rules in the cluster and the most common predicates in the antecedents of those rules;

• consequent: consequents of the rules;

• other numeric columns from the input nugget, aggregated by mean within each cluster.

Arguments

x

A nugget of flavour associations, typically the output of dig_associations().

n

The number of clusters to create. Must be a positive integer.

by

A tidyselect expression (see tidyselect syntax) specifying the numeric column to use for clustering.

algorithm

The k-means algorithm to use. One of "Hartigan-Wong" (the default), "Lloyd", "Forgy", or "MacQueen". See stats::kmeans() for details.

predicates_in_label

The number of most common predicates to include in the cluster label. The default is 2.

Author

Michal Burda

Details

Each cluster is represented by a label consisting of the number of rules in the cluster and the most common predicates in the antecedents of those rules.

See Also

dig_associations(), association_matrix() stats::kmeans()

Examples

# Prepare the data
cars <- mtcars |>
    partition(cyl, vs:gear, .method = "dummy") |>
    partition(carb, .method = "crisp", .breaks = c(0, 3, 10)) |>
    partition(mpg, disp:qsec, .method = "triangle", .breaks = 3)

# Search for associations
rules <- dig_associations(cars,
                          antecedent = everything(),
                          consequent = everything(),
                          max_length = 3,
                          min_support = 0.2,
                          measures = c("lift", "conviction"))

# Cluster the found rules
clu <- cluster_associations(rules, 10, "lift")

if (FALSE) {
# Plot the clustered rules
library(ggplot2)

ggplot(clu) +
   aes(x = cluster_label, y = consequent, color = lift, size = support) +
   geom_point() +
   xlab("predicates in antecedent groups") +
   scale_y_discrete(limits = rev) +
   theme(axis.text.x = element_text(angle = 45, hjust = 1))
}