# weclat

0th

Percentile

##### Mining Associations from Weighted Transaction Data with Eclat (WARM)

Find frequent itemsets with the Eclat algorithm. This implementation uses optimized tidlist joins and transaction weights to implement weighted association rule mining (WARM).

Keywords
models
##### 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.

##### Details

Transaction weights are stored in the transaction as a column called weight in transactionInfo.

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).

Note that ECLAT only mines (weighted) frequent itemsets. Weighted association rules can be created using ruleInduction.

##### Value

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

##### Note

The C code can be interrupted by CTRL-C. This is convenient but comes at the price that the code cannot clean up its internal memory.

##### References

G.D. Ramkumar, S. Ranka, and S. Tsur (1998). Weighted Association Rules: Model and Algorithm, Proceedings of ACM SIGKDD

Class transactions, function ruleInduction, eclat

• weclat
• WARM
• warm
##### Examples
# NOT RUN {
data(SunBai)
SunBai

## weights are stored in transactionInfo
transactionInfo(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)
# }

Documentation reproduced from package arules, version 1.5-4, License: GPL-3

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