# R: Condense training set for k-NN classifier

condense {class} | R Documentation

## Condense training set for k-NN classifier

### Description

Condense training set for k-NN classifier

### Usage

    condense(train, class, store, trace = TRUE)

### Arguments

`train` | matrix for training set
`class` | vector of classifications for test set
`store` | initial store set. Default one randomly chosen element of the set.
`trace` | logical. Trace iterations?

### Details

The store set is used to 1-NN classify the rest, and misclassified patterns are added to the store set. The whole set is checked until no additions occur.

### Value

index vector of cases to be retained (the final store set).

### References

P. A. Devijver and J. Kittler (1982) Pattern Recognition. A Statistical Approach. Prentice-Hall, pp. 119–121.

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

### See Also

`reduce.nn`, `multiedit`

### Examples

    data(iris3)
    train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
    test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
    cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
    keep <- condense(train, cl)
    knn(train[keep, , drop=FALSE], test, cl[keep])
    keep2 <- reduce.nn(train, keep, cl)
    knn(train[keep2, , drop=FALSE], test, cl[keep2])

[Package class version 7.2-36 Index]