sequencerules-class

0th

Percentile

Class "sequencerules" --- Collections of Sequential Rules

Represents a collection of sequential rules and their associated quality measure. That is, the elements in the consequent occur at a later time than the elements of the antecedent.

Keywords
classes
Note

Some of the methods for sequences are not implemented as objects of this class can be coerced to sequences.

Objects from the Class

Typically objects are created by a sequence rule mining algorithm as the result value, e.g. method ruleInduction.

Objects can be created by calls of the form new("sequencerules", ...).

Slots

elements:

an object of class itemsets containing a sparse representation of the unique elements of a sequence.

lhs:

an object of class '>sgCMatrix containing a sparse representation of the left-hand sides of the rules (antecedent sequences).

rhs:

an object of class '>sgCMatrix containing a sparse representation of the right-hand sides of the rules (consequent sequences).

ruleInfo:

a data.frame which may contain additional information on a sequence rule.

quality:

a data.frame containing the quality measures of a sequence rule.

Extends

Class "associations", directly.

Methods

coerce

signature(from = "sequencerules", to = "list")

coerce

signature(from = "sequencerules", to = "data.frame")

coerce

signature(from = "sequencerules", to = "sequences"); coerce a collection of sequence rules to a collection of sequences by appending to each left-hand (antecedent) sequence its right-hand (consequent) sequence.

c

signature(x = "sequencerules")

coverage

signature(x = "sequencerules"); returns the support values of the left-hand side (antecedent) sequences.

duplicated

signature(x = "sequencerules")

labels

signature(x = "sequencerules")

ruleInfo

signature(object = "sequencerules")

ruleInfo<-

signature(object = "sequencerules")

inspect

signature(x = "sequencerules")

is.redundant

signature(x = "sequencerules"); returns a logical vector indicating if a rule has a proper subset in x which has the same right-hand side and the same or a higher confidence.

labels

signature(object = "sequencerules")

length

signature(x = "sequencerules")

lhs

signature(x = "sequencerules")

match

signature(x = "sequencerules")

rhs

signature(x = "sequencerules")

show

signature(object = "sequencerules")

size

signature(x = "sequencerules")

subset

signature(x = "sequencerules")

summary

signature(object = "sequencerules")

unique

signature(x = "sequencerules")

See Also

Class '>sgCMatrix, itemsets, associations, '>sequences, method ruleInduction, is.redundant, function cspade

Aliases
  • sequencerules-class
  • coerce,sequencerules,list-method
  • coerce,sequencerules,data.frame-method
  • coerce,sequencerules,sequences-method
  • generatingItemsets
  • coverage,sequencerules-method
  • coverage,sequencerules,ANY,missing-method
  • is.redundant,sequencerules-method
  • summary,sequencerules-method
  • show,sequencerules-method
  • show,summary.sequencerules-method
  • summary.sequencerules-class
  • summary,sequencerules-method
Examples
# NOT RUN {
## continue example
example(ruleInduction, package = "arulesSequences")
cbind(as(r2, "data.frame"), 
      coverage = coverage(r2))

## coerce to sequences
as(as(r2, "sequences"), "data.frame")

## find redundant rules
is.redundant(r2, measure = "lift")
# }
Documentation reproduced from package arulesSequences, version 0.2-19, License: GPL-2

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