| Type: | Package |
| Title: | Numerical Association Rule Mining using Population-Based Nature-Inspired Algorithms |
| Version: | 0.2.0 |
| Classification/ACM: | G.4, H.2.8 |
| Description: | Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>. |
| URL: | https://github.com/firefly-cpp/niarules |
| BugReports: | https://github.com/firefly-cpp/niarules/issues |
| Depends: | R (≥ 4.0.0) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.0 |
| Imports: | stats, utils |
| Suggests: | testthat |
| NeedsCompilation: | no |
| Packaged: | 2025-02-20 16:12:49 UTC; iztok |
| Author: | Iztok Jr. Fister 
[aut, cre, cph] |
| Maintainer: | Iztok Jr. Fister <iztok@iztok.space> |
| Repository: | CRAN |
| Date/Publication: | 2025-02-20 17:00:02 UTC |
Add an attribute to the "rule" list.
Description
This function adds an attribute to the existing list.
Usage
add_attribute(rules, name, type, border1, border2, value)
Arguments
rules |
The current rules list. |
name |
The name of the feature in the rule. |
type |
The type of the feature in the rule. |
border1 |
The first border value in the rule. |
border2 |
The second border value in the rule. |
value |
The value associated with the rule. |
Value
The updated rules list.
Examples
rules <- list()
new_rules <- add_attribute(rules, "feature1", "numerical", 0.2, 0.8, "EMPTY")
Build rules based on a candidate solution.
Description
This function takes a candidate solution vector and a features list and builds rule.
Usage
build_rule(solution, features)
Arguments
solution |
The solution vector. |
features |
The features list. |
Value
A rule.
Calculate the border value based on feature information and a given value.
Description
This function calculates the border value for a feature based on the feature information and a given value.
Usage
calculate_border(feature_info, value)
Arguments
feature_info |
Information about the feature. |
value |
The value to calculate the border for. |
Value
The calculated border value.
Examples
feature_info <- list(type = "numerical", lower_bound = 0, upper_bound = 1)
border_value <- calculate_border(feature_info, 0.5)
Calculate the fitness of an association rule.
Description
This function calculates the fitness of an association rule using support and confidence.
Usage
calculate_fitness(supp, conf)
Arguments
supp |
The support of the association rule. |
conf |
The confidence of the association rule. |
Value
The fitness of the association rule.
Calculate the selected category based on a value and the number of categories.
Description
This function calculates the selected category based on a given value and the total number of categories.
Usage
calculate_selected_category(value, num_categories)
Arguments
value |
The value to calculate the category for. |
num_categories |
The total number of categories. |
Value
The calculated selected category.
Examples
selected_category <- calculate_selected_category(0.3, 5)
Check if the attribute conditions are satisfied for an instance.
Description
This function checks if the attribute conditions specified in the association rule are satisfied for a given instance row.
Usage
check_attribute(attribute, instance_row)
Arguments
attribute |
An attribute with type and name information. |
instance_row |
A row representing an instance in the dataset. |
Value
TRUE if conditions are satisfied, FALSE otherwise.
Calculate the cut point for an association rule.
Description
This function calculates the cut point, denoting which part of the vector belongs to the antecedent and which to the consequent of the mined association rule.
Usage
cut_point(sol, num_attr)
Arguments
sol |
The cut value from the solution vector. |
num_attr |
The number of attributes in the association rule. |
Value
The cut point value.
Implementation of Differential Evolution metaheuristic algorithm.
Description
This function uses Differential Evolution, a stochastic population-based optimization algorithm, to find the optimal numerical association rule.
Usage
differential_evolution(
d = 10,
np = 10,
f = 0.5,
cr = 0.9,
nfes = 1000,
features,
data,
is_time_series = FALSE
)
Arguments
d |
Dimension of the problem (default: 10). |
np |
Population size (default: 10). |
f |
The differential weight, controlling the amplification of the difference vector (default: 0.5). |
cr |
The crossover probability, determining the probability of a component being replaced (default: 0.9). |
nfes |
The maximum number of function evaluations (default: 1000). |
features |
A list containing information about features, including type and bounds. |
data |
A data frame representing instances in the dataset. |
is_time_series |
A boolean indicating whether the dataset is time series. |
Value
A list containing the best solution, its fitness value, and the number of function evaluations and list of identified association rules.
Evaluate a candidate solution, with optional time series filtering.
Description
This function evaluates the fitness of an association rule using support and confidence. If time series data is used, it restricts evaluation to the specified time range.
Usage
evaluate(solution, features, instances, is_time_series = FALSE)
Arguments
solution |
A vector representing a candidate solution. |
features |
A list containing information about features. |
instances |
A data frame representing dataset instances. |
is_time_series |
A boolean flag indicating if time series filtering is required. |
Value
A list containing fitness and identified rules.
Extract feature information from a dataset, excluding timestamps.
Description
This function analyzes the given dataset and extracts information about each feature.
Usage
extract_feature_info(data, timestamp_col = "timestamp")
Arguments
data |
The dataset to analyze. |
timestamp_col |
Optional. The name of the timestamp column to exclude from features. |
Value
A list containing information about each feature, including type and bounds/categories.
Get the position of a feature.
Description
This function returns the position of a feature in the vector, considering the type of the feature.
Usage
feature_position(features, feature)
Arguments
features |
The features list. |
feature |
The name of the feature to find. |
Value
The position of the feature.
Examples
features <- list(
feature1 = list(type = "numerical"),
feature2 = list(type = "categorical"),
feature3 = list(type = "numerical")
)
position <- feature_position(features, "feature2")
Fix Borders of a Numeric Vector
Description
This function ensures that all values greater than 1.0 are set to 1.0, and all values less than 0.0 are set to 0.0.
Usage
fix_borders(vector)
Arguments
vector |
A numeric vector to be processed. |
Value
A numeric vector with borders fixed.
Format Rule Parts
Description
This function formats the parts of an association rule into a string.
Usage
format_rule_parts(parts)
Arguments
parts |
A list containing parts of an association rule. |
Value
A formatted string representing the rule parts.
Map solution boundaries to time series instances.
Description
This function maps the lower and upper bounds of the solution vector to a subset of the dataset.
Usage
map_to_ts(lower, upper, instances)
Arguments
lower |
The lower bound in [0, 1]. |
upper |
The upper bound in [0, 1]. |
instances |
The full dataset. |
Value
A list with 'low', 'up', and 'filtered_instances'.
Print Numerical Association Rules
Description
This function prints association rules including antecedent, consequence, support, confidence, and fitness. For time series datasets, it also includes the start and end timestamps instead of indices.
Usage
print_association_rules(rules, is_time_series = FALSE, timestamps = NULL)
Arguments
rules |
A list containing association rules. |
is_time_series |
A boolean flag indicating if time series information should be included. |
timestamps |
A vector of timestamps corresponding to the time series data. |
Value
Prints the association rules.
Print feature information extracted from a dataset.
Description
This function prints the information extracted about each feature.
Usage
print_feature_info(feature_info)
Arguments
feature_info |
The list containing information about each feature. |
Value
A message is printed to the console for each feature, providing information about the feature's type, and additional details such as lower and upper bounds for numerical features, or categories for categorical features. No explicit return value is generated.
Calculate the dimension of the problem, excluding timestamps.
Description
Calculate the dimension of the problem, excluding timestamps.
Usage
problem_dimension(feature_info, is_time_series = FALSE)
Arguments
feature_info |
A list containing information about each feature. |
is_time_series |
Boolean indicating if time series data is present. |
Value
The calculated dimension based on the feature types.
Read a CSV Dataset
Description
Reads a dataset from a CSV file and optionally parses a timestamp column.
Usage
read_dataset(
dataset_path,
timestamp_col = "timestamp",
timestamp_formats = c("%d/%m/%Y %H:%M:%S", "%H:%M:%S %d/%m/%Y")
)
Arguments
dataset_path |
A string specifying the path to the CSV file. |
timestamp_col |
A string specifying the timestamp column name (default: '"timestamp"'). |
timestamp_formats |
A vector of date-time formats to try for parsing timestamps. |
Value
A data frame containing the dataset.
Simple Random Search
Description
This function generates a vector of random solutions for a specified length.
Usage
rs(candidate_len)
Arguments
candidate_len |
The length of the vector of random solutions. |
Value
A vector of random solutions between 0 and 1.
Examples
candidate_len <- 10
random_solutions <- rs(candidate_len)
print(random_solutions)
Calculate support and confidence for an association rule.
Description
This function calculates the support and confidence for the given antecedent and consequent in the dataset instances.
Usage
supp_conf(antecedent, consequent, instances, features)
Arguments
antecedent |
The antecedent part of the association rule. |
consequent |
The consequent part of the association rule. |
instances |
A data frame representing instances in the dataset. |
features |
A list containing information about features, including type and bounds. |
Value
A list containing support and confidence values.
Write Association Rules to CSV file
Description
This function writes association rules to a CSV file. For time series datasets, it also includes start and end timestamps instead of indices.
Usage
write_association_rules_to_csv(
rules,
file_path,
is_time_series = FALSE,
timestamps = NULL
)
Arguments
rules |
A list of association rules. |
file_path |
The file path for the CSV output. |
is_time_series |
A boolean flag indicating if time series information should be included. |
timestamps |
A vector of timestamps corresponding to the time series data. |
Value
No explicit return value. The function writes association rules to a CSV file.