# Classifies data into two groups using a clustering regime.

### Description

This function is primarily designed to split a bimodal data vector into two groups to allow the fitting of mixture generalised lambda distributions.

### Usage

1 | ```
fun.class.regime.bi(data, perc.cross, fun.cross)
``` |

### Arguments

`data` |
Data to be classified into two groups. |

`perc.cross` |
Percentage of cross over from one data to the other, usually set at 1% |

`fun.cross` |
Any clustering function such as |

### Details

This function is part of the routine mixture fitting procedure provided in this
package. The `perc.cross`

argument or percentage of cross over is designed
to allow the use of maximum likelihood estimation via EM algorithm for fitting
bimodal data. When this is invoked, it will ensure both part of the data will
contain both the minmum and maximum of the data set as well as a proportion (
specified in perc.cross argument) of observations from each other. If 1% is
required, then data.a will contains 1% of the data.b and vice versa after the
full data set has been classified into `data.a`

and `data.b`

by the
`fun.cross`

classification regime.

### Value

`data.a` |
First group of data obtained by the classification algorithm. |

`data.b` |
Second group of data obtained by the classification algorithm. |

### Author(s)

Steve Su

### References

Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.

Su (2006) Maximum Log Likelihood Estimation using EM Algorithm and Partition Maximum Log Likelihood Estimation for Mixtures of Generalized Lambda Distributions. Working Paper.

### See Also

`link{clara}`

, `pam`

, `fanny`

### Examples

1 2 3 4 5 | ```
## Classify the faithful[,1] data into two categories with 10% cross over mix.
# fun.class.regime.bi(faithful[,1],0.1,clara)
## Classify the faithful[,1] data into two categories with no mixing:
# fun.class.regime.bi(faithful[,1],0,clara)
``` |