sdbscan
v0.3.3
Published
DBSCAN implementation (density based spatial clustering) for javascript. Works in node and browser
Downloads
27
Maintainers
solzimerReadme
sdbscan
Super fast density based spatial clustering DBSCAN implementation for unidimiensional and multidimensional data. Works on nodejs and browser.
Installation
npm install sdbscanUsage
NodeJS
const sdbscan = require("sdbscan");
var data = [0, 1, 100, 101, 2, 102, 3, 104, 4, 103, 105, 5];
var res = sdbscan(data,2,3);Browser
<!doctype html>
<html>
<head>
<script src="sdbscan.js"></script>
</head>
<body>
<script>
var data = [0,1,100,101,2,102,3,104,4,103,105,5];
var res = sdbscan(data,2,3);
console.log(data);
console.log(res);
</script>
</body>
</html>Results
{
"noise": [],
"clusters": [
{
"id": 0,
"data": [5,4,3,2,1,0]
},
{
"id": 1,
"data": [105,103,104,102,101,100]
}
]
}API
sdbscan(data,epsilon,min)
Calculates unidimiensional and multidimensional dbscan clustering on data. Parameters are:
- data Unidimiensional or multidimensional array of values to be clustered. for unidimiensional data, takes the form of a simple array [1,2,3.....,n]. For multidimensional data, takes a NxM array [[1,2],[2,3]....[n,m]]
- epsilon Maximum distance for two points to be considered in the same region.
- min Minimal region size. If a region for a point is lesser than min, this point will be considered as noise (cannot be included in any group).
The function will return an object with the following data:
- noise Points that cannot be added to any cluster.
- clusters An array of clusters, with an ID and the data points belonging to it.