NBMinerParameters

0th

Percentile

Estimate Global Model Parameters from Data

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

Keywords
models
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 $\pi$.
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, transactions-class

Aliases
  • NBMinerParameters
Examples
data("Epub")

param <- NBMinerParameters(Epub, trim = 0.05, plot = TRUE, verbose = TRUE)
param
Documentation reproduced from package arulesNBMiner, version 0.1-5, License: GPL-2

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