Go Kmeans

This is a simple implementation of the Elkan's Kmeans algorithm in Go.

Installing

$ go get github.com/arjunsk/kmeans

Usage

package main

import (
	"fmt"
	"github.com/arjunsk/kmeans"
	"github.com/arjunsk/kmeans/elkans"
)

func main() {
	vectorList := [][]float64{
		{1, 2, 3, 4},
		{1, 2, 4, 5},
		{1, 2, 4, 5},
		{1, 2, 3, 4},
		{1, 2, 4, 5},
		{1, 2, 4, 5},
		{10, 2, 4, 5},
		{10, 3, 4, 5},
		{10, 5, 4, 5},
		{10, 2, 4, 5},
		{10, 3, 4, 5},
		{10, 5, 4, 5},
	}

	clusterer, err := elkans.NewKMeans(vectorList, 2,
		500, 0.5,
		kmeans.L2Distance, kmeans.KmeansPlusPlus, false)
	if err != nil {
		panic(err)
	}

	centroids, err := clusterer.Cluster()
	if err != nil {
		panic(err)
	}

	for _, centroid := range centroids {
		fmt.Println(centroid)
	}
	/*
	[1 2 3.6666666666666665 4.666666666666666]
	[10 3.333333333333333 4 5]
	*/
}

FAQ

Read More

What should be the ideal Centroids Count?

Based on the recommendations from PGVector IVF INDEX, the idea K should

Choose an appropriate number of K - a good place to start is rows / 1000 for up to 1M rows and sqrt(rows) for over 1M rows