NBMinerParameters
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 NBprecise rules instead of NBfrequent 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 modelbased frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery,13(2):137166, September 2006.
See Also
Examples
data("Epub")
param < NBMinerParameters(Epub, trim = 0.05, plot = TRUE, verbose = TRUE)
param