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kmeans

(★ 477)

k-means clustering algorithm implementation written in Go

파일 탐색기

최종 버전 다운로드 (.zip)

- - build.yml
  - coverage.yml
  - lint-soft.yml
  - lint.yml
- FUNDING.yml
- - main.go
- - main.go
- .gitignore
- go.mod
- go.sum
- plotter.go
.gitignore
.golangci-soft.yml
.golangci.yml
go.mod
go.sum
kmeans.gif
kmeans.go
kmeans_test.go
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/muesli/kmeans

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd kmeans

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Go

보통 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Go Go 언어 컴파일러 및 도구 모음입니다.

go build ./...

Go 프로그램을 컴파일해 실행 파일을 만듭니다.

go run .

따로 빌드하지 않고 바로 Go 프로그램을 실행합니다.

✅ 빌드가 에러 없이 끝나면 성공입니다. go run . 을 썼다면 터미널 출력을 확인하세요.

// repository documentation

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# kmeans

[![Latest Release](https://img.shields.io/github/release/muesli/kmeans.svg)](https://github.com/muesli/kmeans/releases)
[![Build Status](https://github.com/muesli/kmeans/workflows/build/badge.svg)](https://github.com/muesli/kmeans/actions)
[![Coverage Status](https://coveralls.io/repos/github/muesli/kmeans/badge.svg?branch=master)](https://coveralls.io/github/muesli/kmeans?branch=master)
[![Go ReportCard](https://goreportcard.com/badge/muesli/kmeans)](https://goreportcard.com/report/muesli/kmeans)
[![GoDoc](https://godoc.org/github.com/golang/gddo?status.svg)](https://pkg.go.dev/github.com/muesli/kmeans)

k-means clustering algorithm implementation written in Go

## What It Does

[k-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) partitions
a multi-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.

![kmeans animation](https://github.com/muesli/kmeans/blob/master/kmeans.gif)

## When Should I Use It?

- When you have numeric, multi-dimensional data sets
- You don't have labels for your data
- You know exactly how many clusters you want to partition your data into

## Example

```go
import (
	"github.com/muesli/kmeans"
	"github.com/muesli/clusters"
)

// set up a random two-dimensional data set (float64 values between 0.0 and 1.0)
var d clusters.Observations
for x := 0; x < 1024; x++ {
	d = append(d, clusters.Coordinates{
		rand.Float64(),
		rand.Float64(),
	})
}

// Partition the data points into 16 clusters
km := kmeans.New()
clusters, err := km.Partition(d, 16)

for _, c := range clusters {
	fmt.Printf("Centered at x: %.2f y: %.2f\n", c.Center[0], c.Center[1])
	fmt.Printf("Matching data points: %+v\n\n", c.Observations)
}
```

## Complexity

If `k` (the amount of clusters) and `d` (the dimensions) are fixed, the problem
can be exactly solved in time O(n<sup>dk+1</sup>), 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.

## Options

You can greatly reduce the running time by adjusting the required delta
threshold. With the following options the algorithm finishes when less than 5%
of the data points shifted their cluster assignment in the last iteration:

```go
km, err := kmeans.NewWithOptions(0.05, nil)
```

The default setting for the delta threshold is 0.01 (1%).

If you are working with two-dimensional data sets, kmeans can generate
beautiful graphs (like the one above) for each iteration of the algorithm:

```go
km, err := kmeans.NewWithOptions(0.01, plotter.SimplePlotter{})
```

Careful: this will generate PNGs in your current working directory.

You can write your own plotters by implementing the `kmeans.Plotter` interface.