This class implements the structure needed to store association rules and the methods associated.
Methods
Method new()
Initialize with an optional name
Details
Creates and initialize a new RuleSet object. It can be done in two ways:
initialize(name, attributes, lhs, rhs, quality)
or initialize(rules)
In the first way, the only mandatory argument is attributes, (character vector) which is a vector of names of the attributes on which we define the rules.
The other way is used to initialize the RuleSet object from a rules object from package arules.
Returns
A new RuleSet object.
Method get_attributes()
Get the names of the attributes
Returns
A character vector with the names of the attributes used in the implications.
Method [()
Get a subset of the rule set
Arguments
idx(integer or logical vector) Indices of the rules to extract or remove. If logical vector, only TRUE elements are retained and the rest discarded.
Returns
A new RuleSet with only the rules given by the idx indices.
Method to_arules()
Convert to arules format
Usage
RuleSet$to_arules(quality = TRUE)
Arguments
quality(logical) Compute/include the interest measures for each rule?
Returns
A rules object as used by package arules.
Method add()
Add a precomputed rule set
Arguments
...An RuleSet object, or a pair lhs, rhs of dgCMatrix.
Returns
Nothing, just updates the internal field.
Method cardinality()
Cardinality: Number of implications in the set
Returns
The cardinality of the implication set.
Method is_empty()
Empty set
Returns
TRUE if the set of implications is empty, FALSE otherwise.
Method size()
Size: number of attributes in each of LHS and RHS
Returns
A vector with two components: the number of attributes present in each of the LHS and RHS of each implication in the set.
Print all rules to text
Returns
A string with all the rules in the set.
Method get_quality()
Get quality metrics
Returns
A data.frame with the quality metrics for each rule.
Method to_latex()
Export to LaTeX
Usage
RuleSet$to_latex(
print = TRUE,
ncols = 1,
numbered = TRUE,
numbers = seq_len(self$cardinality())
)
Arguments
print(logical) Print to output?
ncols(integer) Number of columns for the output.
numbered(logical) If TRUE (default), implications will be numbered in the output.
numbers(vector) If numbered, use these elements to enumerate the implications. The default is to enumerate 1, 2, ..., but can be changed.
Returns
A string in LaTeX format that prints nicely all the implications.
Method get_LHS_matrix()
Get internal LHS matrix
Returns
A sparse matrix representing the LHS of the implications in the set.
Method get_RHS_matrix()
Get internal RHS matrix
Returns
A sparse matrix representing the RHS of the implications in the set.
Filter implications by attributes in LHS and RHS
Usage
RuleSet$filter(lhs = NULL, rhs = NULL, drop = FALSE)
Arguments
lhs(character vector) Names of the attributes to filter the LHS by. If NULL, no filtering is done on the LHS.
rhs(character vector) Names of the attributes to filter the RHS by. If NULL, no filtering is done on the RHS.
drop(logical) Remove the rest of attributes in RHS?
Returns
An RuleSet that is a subset of the current set, only with those rules which has the attributes in lhs and rhs in their LHS and RHS, respectively.
Method get_implications()
Usage
RuleSet$get_implications()
Method support()
Compute support of each implication
Returns
A vector with the support of each implication
Method clone()
The objects of this class are cloneable with this method.
Usage
RuleSet$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.