R: Simulate a Random Transaction Data Set

random.transactions {arules} | R Documentation

## 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.

### Details

The function generates a `nitems` times `ntrans` transaction database.

Currently two simulation methods are implemented:

method `"independent"` (see Hahsler et al., 2006)

  All items are treated as independent. The transaction size is determined by rpois(lambda-1)+1, where `lambda` can be specified (defaults to 3). Note that one subtracted from lambda and added to the size to avoid empty transactions. The items in the transactions are randomly chosen using the numeric probability vector `iProb` of length `nItems` (default: 0.01 for each item).

method `"agrawal"` (see Agrawal and Srikant, 1994)

  This method creates transactions with correlated items uses the following additional parameters:

  lTrans

    average length of transactions.

  nPats

    number of patterns (potential maximal frequent itemsets) used.

  lPats

    average length of patterns.

  corr

    correlation between consecutive patterns.

  cmean

    mean of the corruption level (normal distr.).

  cvar

    variance of the corruption level.

  The simulation is a two-stage process. First, a set of `nPats` patterns (potential maximal frequent itemsets) is generated. The length of the patterns is Poisson distributed with mean `lPats` and consecutive patterns share some items controlled by the correlation parameter `corr`. For later use, for each pattern a pattern weight is generated by drawing from an exponential distribution with a mean of 1 and a corruption level is chosen from a normal distribution with mean `cmean` and variance `cvar`.

  The patterns are created using the following function:

  `random.patterns(nItems, nPats = 2000, method = "agrawal", lPats = 4, corr = 0.5, cmean = 0.5, cvar = 0.1, iWeight = NULL, verbose = FALSE)`

  The function returns the patterns as an `itemsets` objects which can be supplied to `random.transactions` as the argument `patterns`. If no argument `patterns` is supplied, the default values given above are used.

  In the second step, the transactions are generated using the patterns. The length the transactions follows a Poisson distribution with mean `lPats`. For each transaction, patterns are randomly chosen using the pattern weights till the transaction length is reached. For each chosen pattern, the associated corruption level is used to drop some items before adding the pattern to the transaction.

### Value

Returns an object of class `transactions`.

### Author(s)

Michael Hahsler

### 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,
       lambda = 5, iProb = seq(0.2,0.0001, length=200))

    ## size distribution
    summary(size(trans))

    ## 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 (we just create 100 transactions here to speed things up).
    patterns <- random.patterns(nItems = 1000)
    summary(patterns)

    trans2 <- random.transactions(nItems = 1000, nTrans = 100,
       method = "agrawal", patterns = patterns)
    image(trans2)

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

[Package arules version 1.6-2 Index]