arules (version 1.3-0)

weclat: Mining Associations from Weighted Transaction Data with Eclat

Description

Find frequent itemsets with the Eclat algorithm. This implementation uses optimized tidlist joins and transaction weights.

Usage

weclat(data, parameter = NULL, control = NULL)

Arguments

data

an object that can be coerced into an object of class transactions.

parameter

an object of class ASparameter (default values: support = 0.1, minlen = 1L, and maxlen = 5L) or a named list with corresponding components.

control

an object of class AScontrol (default values: verbose = TRUE) or a named list with corresponding components.

Value

• Returns an object of class itemsets. Note that weighted support is returned in quality as column support.

Details

The weighted support of an itemset is the sum of the weights of the transactions that contain the itemset. An itemset is frequent if its weighted support is equal or greater than the threshold specified by support (assuming that the weights sum to one).

See Also

Class transactions, function ruleInduction, eclat

Examples

data(SunBai)
summary(SunBai)

## mine weighted support itemsets using transaction support in SunBai
s <- weclat(SunBai, parameter = list(support = 0.3),
		       control = list(verbose = TRUE))
inspect(sort(s))

## create rules using weighted support (satisfying a minimum 
## weighted confidence of 90%).
r <- ruleInduction(s, confidence = .9)
inspect(r)