arules (version 1.0-12)

random.transactions: Simulate a Random Transaction Data Set

Description

Simulates a random transactions object using different methods.

Usage

random.transactions(nItems, nTrans, method = "independent", ...,
	verbose = FALSE)

Arguments

nItems

an integer. Number of items.

nTrans

an integer. Number of transactions.

method

name of the simulation method used (default: all items occur independently).

...

further arguments used for the specific simulation method (see details).

verbose

report progress.

Value

• Returns an object of class transactions.

Details

The function generates a nitems times ntrans transaction database. Currently two simulation methods are implemented: [object Object],[object Object]

References

Michael Hahsler, Kurt Hornik, and Thomas Reutterer (2006). Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nuernberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization, pages 598--605. Springer-Verlag.

Rakesh Agrawal and Ramakrishnan Srikant (1994). Fast algorithms for mining association rules in large databases. In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487--499, Santiago, Chile.

See Also

transactions-class.

Examples

## generate random 1000 transactions for 200 items with 
## a success probability decreasing from 0.2 to 0.0001
## using the method described in Hahsler et al. (2006).
trans <- random.transactions(nItems = 200, nTrans = 1000, 
   iProb = seq(0.2,0.0001, length=200))

## display random data set
image(trans)

## use the method by Agrawal and Srikant (1994) to simulate transactions 
## which contains correlated items. This should create data similar to
## T10I4D100K (just only 1000 transactions)
patterns <- random.patterns(nItems = 1000)
summary(patterns)

trans2 <- random.transactions(nItems = 1000, nTrans = 1000, 
   method = "agrawal", patterns = patterns)
image(trans2) 

## plot data with items ordered by item frequency
image(trans2[,order(itemFrequency(trans2), decreasing=TRUE)])