cluster

Simple bottom up agglomerative cluster library that supports one and two dimensional points. For computing the distance between two dimensional points we use the Euclidian distance.

Api example

One dimensional point

package main

import (

    "fmt"

    "github.com/hoenirvili/cluster"
    "github.com/hoenirvili/cluster/dimension/one"
    "github.com/hoenirvili/cluster/distance"

)

func main() {
    points := one.NewDistances(-0.3, 0.1, 0.2, 0.4, 1.6, 1.7, 1.9, 2.0)
    distances := distance.NewDistances(points)
    k := 3
    clusters := cluster.Fit(distances, cluster.SingleLinkage, k)
    fmt.Println(clusters)
}

Two dimensional point

package main

import (
    
    "fmt"

    "github.com/hoenirvili/cluster"
    "github.com/hoenirvili/cluster/dimension/two"
    "github.com/hoenirvili/cluster/distance"

)

func main() {
    points := two.NewDistances(
        []float64{-4, -3, -2, -1, 1, 1, 2, 3, 3, 4},
        []float64{-2, -2, -2, -2, -1, 1, 3, 2, 4, 3},
    )
    k := 3
    distances := distance.NewDistances(points)
    clusters := cluster.Fit(distances, cluster.SingleLinkage, k)
    fmt.Println(clusters)
}