apriorimining: Determine association rules

In quentinseifert/apriorimining: Finds association rules

Description

Mines association rules using the Apriori Algorithm. The function demands a transaction matrix, user specified minimum support and minimum confidence and returns generated association rules. First apriorimining transforms the data into an object of class TransactionData using create_transactionmatrix, then the function passes this object to freq_items in order to find frequent item sets. Finally apriorimining generates association rules from those frequent item sets using rules. These functions can also be accessed seperately. The function apriorimining returns an object of class AssociationRules, which can be analysed using show and summary.

Usage

apriorimining(input, m_sup, m_conf)

Arguments

input	Binary matrix containing transaction data, with rows representing transactions and columns representing items. Can be either logical or numeric, every value has to be either 0 / 1 or FALSE / TRUE (0 or FALSE if item is not bought). Columns should be named.
m_sup	User specified minimum support
m_conf	User specified minimum confidence

Value

Returns an object of class AssociationRules

Examples

## Not run: 
## Load data
data(Groceries)
## Mine rules
x <- apriorimining(Groceries, m_sup = 0.05, m_conf = 0.3)
## use methods
summary(x)

## End(Not run)

quentinseifert/apriorimining documentation built on Dec. 3, 2019, 11:30 p.m.