# cutpoints: Find Cutting Points In pracma: Practical Numerical Math Functions

 cutpoints R Documentation

## Find Cutting Points

### Description

Finds cutting points for vector s of real numbers.

### Usage

``````cutpoints(x, nmax = 8, quant = 0.95)
``````

### Arguments

 `x` vector of real values. `nmax` the maximum number of cutting points to choose `quant` quantile of the gaps to consider for cuts.

### Details

Finds cutting points for vector s of real numbers, based on the gaps in the values of the vector. The number of cutting points is derived from a quantile of gaps in the values. The user can set a lower limit for this number of gaps.

### Value

Returns a list with components `cutp`, the cutting points selected, and `cutd`, the gap between values of `x` at this cutting point.

### Note

Automatically finding cutting points is often requested in Data Mining. If a target attribute is available, Quinlan's C5.0 does a very good job here. Unfortunately, the ‘C5.0’ package (of the R-Forge project “Rulebased Models”) is quite cumbersome to use.

### References

Witten, I. H., and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, San Francisco.

`cut`

### Examples

``````N <- 100; x <- sort(runif(N))
cp <- cutpoints(x, 6, 0.9)
n <- length(cp\$cutp)

# Print out
nocp <- rle(findInterval(x, c(-Inf, cp\$cutp, Inf)))\$lengths
cbind(c(-Inf, cp\$cutp), c(cp\$cutp, Inf), nocp)

# Define a factor from the cutting points
fx <- cut(x, breaks = c(-Inf, cp\$cutp, Inf))

## Not run:
# Plot points and cutting points
plot(x, rep(0, N), col="gray", ann = FALSE)
points(cp\$cutp, rep(0, n), pch="|", col=2)

# Compare with k-means clustering
km <- kmeans(x, n)
points(x, rep(0, N), col = km\$cluster, pch = "+")

##  A 2-dimensional example
x <- y <- c()
for (i in 1:9) {
for (j in 1:9) {
x <- c(x, i + rnorm(20, 0, 0.2))
y <- c(y, j + rnorm(20, 0, 0.2))
}
}
cpx <- cutpoints(x, 8, 0)
cpy <- cutpoints(y, 8, 0)

plot(x, y, pch = 18, col=rgb(0.5,0.5,0.5), axes=FALSE, ann=FALSE)
for (xi in cpx\$cutp) abline(v=xi, col=2, lty=2)
for (yi in cpy\$cutp) abline(h=yi, col=2, lty=2)

km <- kmeans(cbind(x, y), 81)
points(x, y, col=km\$cluster)

## End(Not run)
``````

pracma documentation built on Nov. 10, 2023, 1:14 a.m.