Density-based spatial clustering of applications with noise.
DBSCAN is a clustering algorithm. Give it a collection of values and the algorithm organizes them into groups of nearby values.
For many of us, if we're familiar with clustering algorithms at all, we know about k-means clustering. But one of the challenges with k-means is that you need to specify a number of clusters ("k") in order to use it. Much of the time, we won't know what a reasonable k value is a priori. (In fact, that's often what we want to know in the first place!)
What's nice about DBSCAN is that you don't have to specify a number of clusters to use it. All you need is a function to calculate distance between values and some guidance for what amount of distance is considered "close". DBSCAN also produces more reasonable results than k-means across a variety of different distributions.
import DBSCAN
import simd
let input: [SIMD3<Double>] = [[ 0, 10, 20 ],
[ 0, 11, 21 ],
[ 0, 12, 20 ],
[ 20, 33, 59 ],
[ 21, 32, 56 ],
[ 59, 77, 101 ],
[ 58, 79, 100 ],
[ 58, 76, 102 ],
[ 300, 70, 20 ],
[ 500, 300, 202],
[ 500, 302, 204 ]]
let dbscan = DBSCAN(input)
#if swift(>=5.2)
let (clusters, outliers) = dbscan(epsilon: 10,
minimumNumberOfPoints: 1,
distanceFunction: simd.distance)
#else // Swift <5.2 requires explicit `callAsFunction` method name
let (clusters, outliers) = dbscan.callAsFunction(epsilon: 10,
minimumNumberOfPoints: 1,
distanceFunction: simd.distance)
#endif
print(clusters)
// [ [0, 10, 20], [0, 11, 21], [0, 12, 20] ]
// [ [20, 33, 59], [21, 32, 56] ],
// [ [58, 79, 100], [58, 76, 102], [59, 77, 101] ],
// [ [500, 300, 202], [500, 302, 204] ],
print(outliers)
// [ [ 300, 70, 20 ] ]- Swift 5.1+
Add the DBSCAN package to your target dependencies in Package.swift:
import PackageDescription
let package = Package(
name: "YourProject",
dependencies: [
.package(
url: "https://github.com/NSHipster/DBSCAN",
from: "0.0.1"
),
]
)Then run the swift build command to build your project.
MIT
Mattt (@mattt)