https://github.com/smira/go-point-clustering

(Lat, lon) points fast clustering using DBScan algorithm
https://github.com/smira/go-point-clustering

clustering dbscan geolocation point-clustering

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(Lat, lon) points fast clustering using DBScan algorithm

• Host: GitHub
• URL: https://github.com/smira/go-point-clustering
• Owner: smira
• License: other
• Created: 2015-04-17T14:41:28.000Z (about 11 years ago)
• Default Branch: master
• Last Pushed: 2019-06-27T13:40:53.000Z (about 7 years ago)
• Last Synced: 2025-08-13T18:36:19.713Z (11 months ago)
• Topics: clustering, dbscan, geolocation, point-clustering
• Language: Go
• Size: 33.2 KB
• Stars: 52
• Watchers: 2
• Forks: 16
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Point Clustering

[![Build Status](https://travis-ci.org/smira/go-point-clustering.svg?branch=master)](https://travis-ci.org/smira/go-point-clustering)

[![codecov](https://codecov.io/gh/smira/go-point-clustering/branch/master/graph/badge.svg)](https://codecov.io/gh/smira/go-point-clustering)

[![GoDoc](https://godoc.org/github.com/smira/go-point-clustering?status.svg)](https://godoc.org/github.com/smira/go-point-clustering)

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(Lat, lon) points fast clustering using [DBScan](https://en.wikipedia.org/wiki/DBSCAN) algorithm in Go.

Given set of geo points, this library can find clusters according to specified params. There are several optimizations

applied:

* distance calculation is using "fast" implementations of sine/cosine, with `sqrt` being removed

* to find points within `eps` distance [k-d tree](https://en.wikipedia.org/wiki/K-d_tree) is being used

* edge case handling of identical points being present in the set

## Usage

Build list of points:

```go

    points := cluster.PointList{{30.258387, 59.951557}, {30.434124, 60.029499}, ...}

```

Pick settings for DBScan algorithm:

* `eps` is clustering radius (in kilometers)

* `minPoints` is number of points in `eps`-radius of base point to consider it being part of the cluster

`eps` and `minPoints` together define minimum density of the cluster.

Run DBScan:

```go

    clusters, noise := cluster.DBScan(points, 0.8, 10) // eps is 800m, 10 points minimum in eps-neighborhood

```

`DBScan` function returns list of clusters (each `Cluster` being reference to the list of source `points`) and list

of point indexes which don't fit into any cluster (`noise`).

## License

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