A framework for systematic exploration of association rules (Agrawal (1994)), contrast patterns (Chen (2022)), emerging patterns (Dong (1999)), subgroup discovery (Atzmueller (2015)), and conditional correlations (Hájek (1978)). User-defined functions may also be supplied to guide custom pattern searches.
Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications.
Key Features
- Support for both categorical and numeric data.
- Provides both Boolean and fuzzy logic approach.
- Data preparation functions for easy pre-processing phase.
- Functions for examining associations, conditional correlations, and contrasts among data variables.
- Visualization and pattern post-processing tools.
- Integrated Shiny applications for interactive exploration of discovered patterns.
Installation
To install the stable version of nuggets from CRAN, type the following command within the R session:
install.packages("nuggets")You can also install the development version of nuggets from GitHub with:
install.packages("devtools")
devtools::install_github("beerda/nuggets")To start using the package, load it to the R session with:
Minimal Example
The following example demonstrates how to use nuggets to find association rules in the built-in mtcars dataset:
# Preprocess: dichotomize and fuzzify numeric variables
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 among conditions
rules <- dig_associations(cars,
antecedent = everything(),
consequent = everything(),
max_length = 4,
min_support = 0.1,
measures = c("lift", "conviction"))
# Explore the found rules interactively
explore(rules, cars)