NBMinerParameters {arulesNBMiner}R Documentation

Estimate Global Model Parameters from Data

Description

Estimate the global negative binomial data model used by the NBMiner and create an appropriate parameter object.

Usage

NBMinerParameters(data, trim = 0.01, pi = 0.99, theta = 0.5,
    minlen = 1, maxlen = 5, rules = FALSE,
    plot = FALSE, verbose = FALSE, getdata = FALSE)

Arguments

data the data as a object of class transactions.
trim fraction of incidences to trim off the tail of the frequency distribution of the data.
pi precision threshold π.
theta pruning parameter theta.
minlen minimum number of items in found itemsets (default: 1).
maxlen maximal number of items in found itemsets (default: 5).
rules mine NB-precise rules instead of NB-frequent itemsets?
plot plot the model?
verbose use verbose output for the estimation procedure.
getdata get also the observed and estimated counts.

Details

Uses the EM algorithm to estimate the global NB model for the data. The EM algorithm is used since the zero class (items which do not occur in the dataset) is not included in the data. The result are the two NB parameters k and a, where a is rescaled by dividing it by the number of incidences in the data (this is needed by the NBMiner). Also the real number of items n is a result of the estimation.

theta and pi are just taken and added to the resulting parameter object.

Value

an object of class NBMinerParameter for NBMiner.

References

Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery,13(2):137-166, September 2006.

See Also

NBMiner, transaction-class

Examples

data("Epub")

param <- NBMinerParameters(Epub, trim = 0.001, plot = TRUE, verbose = TRUE)
param

[Package arulesNBMiner version 0.1-0 Index]