clusterInit: Create an instance of ['ClusterInit'] class

In MixAll: Clustering and Classification using Model-Based Mixture Models

Create an instance of [ClusterInit] class

Description

The initialization step is a two stages process: the proper initialization step and some (optionnals) iterations of an algorithm [clusterAlgo].

Usage

clusterInit(
  method = "class",
  nbInit = 5,
  algo = "EM",
  nbIteration = 20,
  epsilon = 0.01
)

Arguments

method	Character string with the initialisation method. Possible values: "random", "class", "fuzzy". Default value is "class".
nbInit	integer defining the number of initialization point to test. Default value is 5.
algo	String with the initialisation algorithm. Possible values: "EM", "CEM", "SEM", "SemiSEM". Default value is "EM".
nbIteration	Integer defining the number of iteration in algo. nbIteration must be a positive integer. Default values is 20. if .
epsilon	threshold to use in order to stop the iterations. Default value is 0.01.

Details

There is three ways to initialize the parameters:

• random: The initial parameters of the mixture are chosen randomly

• class: The initial membership of individuals are sampled randomly

• fuzzy: The initial probabilities of membership of individuals are sampled randomly

A few iterations of an algorithm [clusterAlgo] are then performed. It is strongly recommended to use a few number of iterations of the EM or SEM algorithms after initialization. This allows to detect "bad" initialization starting point.

These two stages are repeated until nbInit is reached. The initial point with the best log-likelihood is conserved as the initial starting point.

Value

a [ClusterInit] object

Author(s)

Serge Iovleff

Examples

 clusterInit(method = "class", nbInit=1, algo="CEM",nbIteration=50, epsilon=0.00001)
 clusterInit(nbIteration=0) # no algorithm

MixAll documentation built on June 22, 2024, 6:56 p.m.