examples | ||
.gitignore | ||
.travis.yml | ||
cluster.go | ||
draw.go | ||
kmeans_test.go | ||
kmeans.gif | ||
kmeans.go | ||
LICENSE | ||
point.go | ||
README.md |
kmeans
k-means clustering algorithm implementation written in Go
What It Does
k-means clustering partitions
an n-dimensional data set into k
clusters, where each data point belongs to the
cluster with the nearest mean, serving as a prototype of the cluster.
Example
import "github.com/muesli/kmeans"
km := kmeans.New()
// set up "random" data points (float64 values between 0.0 and 1.0)
var d kmeans.Points
for x := 0; x < 255; x += 4 {
for y := 0; y < 255; y += 4 {
d = append(d, kmeans.Point{
float64(x) / 255.0,
float64(y) / 255.0,
})
}
}
// Partition the data points into 16 clusters
clusters, err := km.Partition(d, 16)
for _, c := range clusters {
fmt.Printf("Centered at x: %.2f y: %.2f\n", c.Center[0]*255.0, c.Center[1]*255.0)
fmt.Printf("Points: %+v\n\n", c.Points)
}
Complexity
If k
(the amount of clusters) and d
(the dimensions) are fixed, the problem
can be exactly solved in time O(ndk+1), where n
is the number of
entities to be clustered.
The running time of the algorithm is O(nkdi), where n
is the number of
d
-dimensional vectors, k
the number of clusters and i
the number of
iterations needed until convergence. On data that does have a clustering
structure, the number of iterations until convergence is often small, and
results only improve slightly after the first dozen iterations. The algorithm
is therefore often considered to be of "linear" complexity in practice,
although it is in the worst case superpolynomial when performed until
convergence.
You can greatly reduce the running time by adjusting the required delta threshold. The following code executes the algorithm until less than 5% of the data points shifted their cluster assignment in the last iteration:
km, _ := kmeans.NewWithOptions(0.05, false)