pMineR (version 0.31)

secondOrderMarkovModel: A class to train Second Order Markov Models#'

Description

This is an implementation of the Second Order Markov Model (SOMM) for Process Mining issues. This class provides a minimal set of methods to handle with the FOMM model:

• secondOrderMarkovModel( ) is the costructor
• loadDataset( ) loads data taken from a dataLoader::getData() method, into a SOMM object
• trainModel( ) train a model using the previously loaded dataset
• replay( ) re-play a given event log on the internal SOMM model
• play( ) play the internal FOMM model a desired number of times, in order to simulate new event-logs. This methods can also, if desired, simulate event-logs which does not complies with the internal SOMM model.
• getModel( ) return the trained internal SOMM model

In order to better undestand the use of such methods, please visit: www.pminer.info The consturctor admit the following parameters: parameters.list a list containing possible parameters to tune the model.

Usage

secondOrderMarkovModel(parameters.list = list())

Arguments

parameters.list

a list containing the parameters. The possible ones are: 'considerAutoLoop' and 'threshold'. 'considerAutoLoop' is a boolean which indicates if the autoloops have to be admitted, while 'threshold' is the minimum value that a probability should have to do not be set to zero, in the transition matrix.

Examples

## Not run: 
# 
# # create a Loader
# obj.L<-dataLoader();   
# 
# # Load a .csv 
# obj.L$load.csv(nomeFile = "../otherFiles/mammella.csv",
# IDName = "CODICE_SANITARIO_ADT",
# EVENTName = "DESC_REPARTO_RICOVERO",
# dateColumnName = "DATA_RICOVERO")
# 
# # get the loaded data
# dati <- obj.L$getData()
# 
# # build a Second Order Marvov Model with a threshold of 0.2
# SOMM <- secondOrderMarkovModel( 
# parameters.list = list("threshold"=0.002))
# 
# # load the data
# SOMM$loadDataset(dataList = dati)
# 
# # train a model
# SOMM$trainModel()
# 
# # generate 10 new processes (nb: if the 
# # threshold is too low, it can fail...)
# aaa <- SOMM$play(numberOfPlays = 10)
# 
# # get the transition matrix
# TranMatrix <- SOMM$getModel(kindOfOutput = "MM.2.Matrix.perc")
# 
# 
# ## End(Not run)