# confusion_matrix: Confusion Matrix - External Measures, Cluster Stability In clv: Cluster Validation Techniques

## Description

For two different partitioning function computes confusion matrix.

## Usage

 `1` ```confusion.matrix(clust1, clust2) ```

## Arguments

 `clust1` integer `vector` with information about cluster id the object is assigned to. If vector is not integer type, it will be coerced with warning. `clust2` integer `vector` with information about cluster id the object is assigned to. If vector is not integer type, it will be coerced with warning.

## Details

Let P and P' be two different partitioning of the same data. Partitionings are represent as two vectors `clust1, clust2`. Both vectors should have the same length. Confusion matrix measures the size of intersection between clusters comming from P and P' according to equation:

M[i,j] = | intersection of P(i) and P'(j) |

where:

 P(i) - cluster which belongs to partitioning P, P'(j) - cluster which belongs to partitioning P', |A| - cardinality of set A.

## Value

`cls.set.section` returns a n x m integer `matrix` where n = |P| and m = |P'| defined above.

## Author(s)

Lukasz Nieweglowski

Result used in `similarity.index`.

## Examples

 ```1 2 3 4 5 6``` ```# create two different subsamples mx1 <- matrix(as.integer( c(1,2,3,4,5,6,1,1,2,2,3,3) ), 6, 2 ) mx2 <- matrix(as.integer( c(1,2,4,5,6,7,1,1,2,2,3,3) ), 6, 2 ) # find section m = cls.set.section(mx1,mx2) confusion.matrix(as.integer(m[,2]),as.integer(m[,3])) ```

### Example output

```Loading required package: cluster