Simulates a random
transactions object using different
an integer. Number of items.
an integer. Number of transactions.
name of the simulation method used (default: all items occur independently).
further arguments used for the specific simulation method (see details).
The function generates a
Currently two simulation methods are implemented:
"independent"(see Hahsler et al., 2006)
All items are treated as independent. The transaction size
is determined by rpois(lambda-1)+1, where
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
iProb of length
(default: 0.01 for each item).
"agrawal"(see Agrawal and Srikant, 1994)
This method creates transactions with correlated items uses the following additional parameters:
average length of transactions.
number of patterns (potential maximal frequent itemsets) used.
average length of patterns.
correlation between consecutive patterns.
mean of the corruption level (normal distr.).
variance of the corruption level.
The simulation is a two-stage process. First, a set of
(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
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
cmean and variance
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
If no argument
patterns is supplied, the default values given above
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.
Returns an object of class
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.
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## 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)])
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