https://github.com/muesli/clusters

Data structs and algorithms for clustering data observations and basic computations in n-dimensional spaces
https://github.com/muesli/clusters

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Data structs and algorithms for clustering data observations and basic computations in n-dimensional spaces

• Host: GitHub
• URL: https://github.com/muesli/clusters
• Owner: muesli
• License: mit
• Created: 2018-06-02T11:31:34.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-05-16T10:34:05.000Z (about 2 years ago)
• Last Synced: 2025-04-19T12:13:47.790Z (about 2 months ago)
• Topics: hacktoberfest
• Language: Go
• Size: 8.79 KB
• Stars: 18
• Watchers: 2
• Forks: 19
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# clusters

Data structs and algorithms for clustering data observations and basic

computations in n-dimensional spaces.

## Example

```go

import "github.com/muesli/clusters"

// fake some observations

var o clusters.Observations

o = append(o, clusters.Coordinates{1, 1})

o = append(o, clusters.Coordinates{3, 2})

o = append(o, clusters.Coordinates{5, 3})

// seed a new set of clusters

c, err := clusters.New(2, o)

// add observations to clusters

c[0].Append(o[0])

c[1].Append(o[1])

c[1].Append(o[2])

// calculate the centroids for each cluster

c.Recenter()

// find the nearest cluster for an observation

i := c.Nearest(o[1])

// => returns index 1

// find the neighbouring cluster and its average distance for an observation

i, d := c.Neighbour(o[0], 0)

// => returns index 1 with euclidean distance 12.5

```

## Development

[![GoDoc](https://godoc.org/github.com/golang/gddo?status.svg)](https://godoc.org/github.com/muesli/clusters)

[![Build Status](https://travis-ci.org/muesli/clusters.svg?branch=master)](https://travis-ci.org/muesli/clusters)

[![Coverage Status](https://coveralls.io/repos/github/muesli/clusters/badge.svg?branch=master)](https://coveralls.io/github/muesli/clusters?branch=master)

[![Go ReportCard](http://goreportcard.com/badge/muesli/clusters)](http://goreportcard.com/report/muesli/clusters)