Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005) tools:::Rd_expr_doi("10.18637/jss.v014.i15").
Create a transactions object with transactions() or import basket or
single-format data with read.transactions(). Numeric variables should
first be converted to categories with discretize() or discretizeDF().
Inspect the data with summary(), inspect(), itemFrequency(), or
itemFrequencyPlot().
Mine association rules with apriori() or frequent itemsets with
eclat().
Rank and filter patterns with sort(), subset(), and
interestMeasure(). Use is.redundant(), is.closed(), or
is.maximal() for common postprocessing tasks.
Convert results with DATAFRAME() or LIST(), or visualize them with the
suggested package arulesViz.
apriori() mines rules or itemsets and offers detailed control over rule
appearance.
eclat() is designed for mining frequent, closed, or maximal itemsets.
ruleInduction() creates rules from an existing collection of itemsets.
fim4r() provides access to additional mining algorithms when the optional
fim4r package is installed.
transactions stores sparse binary transaction data. Mining functions return itemsets or rules, both derived from associations. Item coding must be compatible when objects are compared or combined; see itemCoding.
Maintainer: Michael Hahsler mhahsler@lyle.smu.edu (ORCID) [copyright holder]
Authors:
Michael Hahsler mhahsler@lyle.smu.edu (ORCID) [copyright holder]
Christian Buchta [copyright holder]
Bettina Gruen [copyright holder]
Kurt Hornik (ORCID) [copyright holder]
Other contributors:
Christian Borgelt [contributor, copyright holder]
Ian Johnson [contributor]
Makhlouf Ledmi [contributor]
arulesViz for visualization and the package vignettes for extended examples.