# Numerical Association Rule Mining using Population-Based Nature-Inspired Algorithms

## Documentation for package ‘niarules’ version 0.3.0

- DESCRIPTION file.

## Help Pages

add_attribute | Add an attribute to the "rule" list.
build_coral_plots | Build coral plot layout (nodes + edges) from a parsed rules object
build_rule | Build rules based on a candidate solution.
calculate_border | Calculate the border value based on feature information and a given value.
calculate_fitness | Calculate the fitness of an association rule.
calculate_selected_category | Calculate the selected category based on a value and the number of categories.
check_attribute | Check if the attribute conditions are satisfied for an instance.
cut_point | Calculate the cut point for an association rule.
differential_evolution | Implementation of Differential Evolution metaheuristic algorithm.
evaluate | Evaluate a candidate solution, with optional time series filtering.
extract_feature_info | Extract feature information from a dataset, excluding timestamps.
feature_position | Get the position of a feature.
fix_borders | Fix Borders of a Numeric Vector
format_rule_parts | Format Rule Parts
map_to_ts | Map solution boundaries to time series instances.
parse_rules | Parse association rules into a reusable, layout-agnostic structure
particle_swarm_optimization | Implementation of Particle Swarm Optimization (PSO) metaheuristic algorithm.
print_association_rules | Print Numerical Association Rules
print_feature_info | Print feature information extracted from a dataset.
problem_dimension | Calculate the dimension of the problem, excluding timestamps.
read_dataset | Read a CSV Dataset
render_coral_rgl | Apply styling to coral plots and render them with rgl
rs | Simple Random Search
supp_conf | Calculate support and confidence for an association rule.
write_association_rules_to_csv | Write Association Rules to CSV file