EfirstOrderMarkovModel: A class to train First Order Markov Models

In robertogattabs/pMiner.v045b: Processes Mining in Medicine

A class to train First Order Markov Models

Description

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

• EfirstOrderMarkovModel( ) is the costructor

• loadDataset( ) loads data taken from a dataLoader::getData() method, into a FOMM object

• trainModel( ) train a model using the previously loaded dataset

• replay( ) re-play a given event log on the internal FOMM 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 FOMM model.

• plot( ) plot the internal model

• getModel( ) return the trained internal FOMM model

• getInstanceClass( ) return the instance class Name and description (version, etc.)

• findReacheableNodes( ) and return the array containing the reacheable states, starting from the passed one.

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

EfirstOrderMarkovModel(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.

robertogattabs/pMiner.v045b documentation built on Aug. 2, 2022, 1:53 p.m.