random.transactions {arules}  R Documentation 
Simulate a Random Transactions
Description
Simulate random transactions using different methods.
Usage
random.transactions(
nItems,
nTrans,
method = "independent",
...,
verbose = FALSE
)
random.patterns(
nItems,
nPats = 2000,
method = NULL,
lPats = 4,
corr = 0.5,
cmean = 0.5,
cvar = 0.1,
iWeight = NULL,
verbose = FALSE
)
Arguments
nItems 
an integer. Number of items to simulate 
nTrans 
an integer. Number of transactions to simulate 
method 
name of the simulation method used (see Details Section). 
... 
further arguments used for the specific simulation method (see details). 
verbose 
report progress? 
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 distribution). 
cvar 
variance of the corruption level. 
iWeight 
item selection weights to build patterns. 
Details
Currently two simulation methods are implemented:

"independent"
(Hahsler et al, 2006): All items are treated as independent. The transaction size is determined byrpois(lambda  1) + 1
, wherelambda
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 vectoriProb
of lengthnItems
(default: 0.01 for each item). 
"agrawal"
(see Agrawal and Srikant, 1994): This method creates transactions with correlated items usingrandom.patters()
. The simulation is a twostage process. First, a set ofnPats
patterns (potential maximal frequent itemsets) is generated. The length of the patterns is Poisson distributed with meanlPats
and consecutive patterns share some items controlled by the correlation parametercorr
. 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 meancmean
and variancecvar
. The function returns the patterns as anitemsets
objects which can be supplied torandom.transactions()
as the argumentpatterns
. If no argumentpatterns
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 a ntrans x nitems
transactions object.
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. SpringerVerlag.
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
Other itemMatrix and transactions functions:
abbreviate()
,
crossTable()
,
c()
,
duplicated()
,
extract
,
hierarchy
,
image()
,
inspect()
,
is.superset()
,
itemFrequencyPlot()
,
itemFrequency()
,
itemMatrixclass
,
match()
,
merge()
,
sample()
,
sets
,
size()
,
supportingTransactions()
,
tidListsclass
,
transactionsclass
,
unique()
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)])