README.md

Gomeans

An implementation of the k-means clustering algorithm in Golang in the cartesian coordinate plane.

Definition

k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Output

Here, k=4, n=800. Each point's coordinates have been randomly generated, explaining this uniform distribution.

Installation

You will need to have go-chart in your $GOPATH to create charts.

$ go get -u github.com/wcharczuk/go-chart

An example of use of the library is in main.go that you can simply execute as follow:

$ go run main.go -k 3 -n 800

Documentation

There are two simple structures: a Point, that can be displayed using its X and Y. And a Cluster, defined by a center and a slice of Point.

type Point struct {
  X float64
  Y float64
}

type Cluster struct {
  Center Point
  Points []Point
}

Then, there are basically two main exposed functions. Run is a simple function that returns a slice of Cluster given a dataset of Point with k int, the number of wanted clusters.

func Run(dataset []Point, k int) []Cluster

And RunWithDrawing that does the same thing except that it also generates graphs of each step of the algorithm in the charts/ folder, as PNG.

func RunWithDrawing(dataset []Point, k int) []Cluster

Algorithm

It is relatively efficient: O(2tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t, d << n. Check out the references for more details about the algorithm.

References

• Data Clustering Algorithms
• K-means clustering

Others

Contributions, issues and questions welcomed.