clusterInit: Create an instance of ['ClusterInit'] class
In MixAll: Clustering and Classification using Model-Based Mixture Models

Description Usage Arguments Details Value Author(s) Examples

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

1 2	clusterInit(method = "class", nbInit = 5, algo = "EM", nbIteration = 20, epsilon = 0.01)

`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.

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.

a [ClusterInit] object

Serge Iovleff