Super fast density based spatial clustering DBSCAN implementation for unidimiensional and multidimensional data. Works on nodejs and browser.
npm install sdbscan
const sdbscan = require("sdbscan");
var data = [0, 1, 100, 101, 2, 102, 3, 104, 4, 103, 105, 5];
var res = sdbscan(data,2,3);<!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>{
"noise": [],
"clusters": [
{
"id": 0,
"data": [5,4,3,2,1,0]
},
{
"id": 1,
"data": [105,103,104,102,101,100]
}
]
}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.
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