# increaseB

Increase reference data sets

## Syntax

``updatedEvaluation = increaseB(evaluation,numsets)``

## Description

example

````updatedEvaluation = increaseB(evaluation,numsets)` returns a gap criterion clustering evaluation object `updatedEvaluation`, which uses the gap criterion clustering evaluation object `evaluation` and an additional number of reference data sets specified by `numsets`.```

## Examples

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Create a gap clustering evaluation object using `evalclusters`. Then, use `increaseB` to increase the number of reference data sets used to compute the gap criterion values.

Load the `fisheriris` data set. The data contains length and width measurements from the sepals and petals of three species of iris flowers.

`load fisheriris`

Cluster the flower measurement data using `kmeans`, and use the gap criterion to evaluate proposed solutions for 1 to 5 clusters. Use 50 reference data sets.

```rng("default") % For reproducibility evaluation = evalclusters(meas,"kmeans","gap","KList",1:5,"B",50)```
```evaluation = GapEvaluation with properties: NumObservations: 150 InspectedK: [1 2 3 4 5] CriterionValues: [0.0870 0.5822 0.8766 1.0007 1.0465] OptimalK: 4 ```

The clustering evaluation object `evaluation` contains data on each proposed clustering solution. The returned results indicate that the optimal number of clusters is four.

The value of the `B` property of `evaluation` shows 50 reference data sets.

`evaluation.B`
```ans = 50 ```

Increase the number of reference data sets by 100, for a total of 150 sets.

`evaluation = increaseB(evaluation,100)`
```evaluation = GapEvaluation with properties: NumObservations: 150 InspectedK: [1 2 3 4 5] CriterionValues: [0.0794 0.5850 0.8738 1.0034 1.0508] OptimalK: 5 ```

The returned results now indicate that the optimal number of clusters is five.

The value of the `B` property of `evaluation` now shows 150 reference data sets.

`evaluation.B`
```ans = 150 ```

## Input Arguments

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Clustering evaluation data, specified as a `GapEvaluation` clustering evaluation object. Create a clustering evaluation object by using `evalclusters`.

Number of additional reference data sets, specified as a positive integer scalar.

Data Types: `single` | `double`

## Output Arguments

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Updated clustering evaluation data, returned as a `GapEvaluation` clustering evaluation object. `updatedEvaluation` contains evaluation data obtained using the reference data sets from the `evaluation` object and a number of additional reference data sets specified by `numsets`.

The `increaseB` function updates the `B` property of the `evaluation` object to reflect the increase in the number of reference data sets used to compute the gap criterion values. The function also updates the `CriterionValues` property with gap criterion values computed using the total number of reference data sets. If the software finds a new optimal number of clusters and optimal clustering solution when using the total number of reference data sets, then `increaseB` updates the `OptimalK` and `OptimalY` properties. The function also updates the `LogW`, `ExpectedLogW`, `StdLogW`, and `SE` properties.

## Version History

Introduced in R2014a