Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More
Socket
Sign inDemoInstall
Socket

data-clustering

Package Overview
Dependencies
Maintainers
1
Versions
6
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

data-clustering

A set of clustering tool for javascript

  • 0.1.0
  • Source
  • npm
  • Socket score

Version published
Weekly downloads
8
decreased by-70.37%
Maintainers
1
Weekly downloads
 
Created
Source

clustering

A set of clustering tool for javascript

Installation

npm
npm install data-clustering
bower
bower install data-clustering

Usage

if using npm:

import * as cl from 'data-clustering';

if using bower

<script src="bower_components/clustering/dist/clustering.js"></script>

What's in it

Clustering Tools

Hierachical Cluster

Example:

	var hc_cluster = cl.HierachicalCluster()
	.data([{
		name : '1',
		value : {
			point : [0, 1]
		}
	},
	{
		name : '2',
		value: {
			point : [0, 2]
		}
	},
	{
		name : '3',
		value: {
			point : [0, 3]
		}
	},
	{
		name : '4',
		value : {
			point : [0, 3]
		}
	},
	{
		name : '5',
		value : {
			point : [0, -1]
		}
	},
	{
		name : '6',
		value : {
			point : [0, 10]
		}
	},
	{
		name : '7',
		value : {
			point : [0, 11]
		}
	}])
	.dist_metric(cl.euclidean_distance)
	.dist_fun('max')
	.save_history(true)
	.init()
	.cluster();

	console.log('root', hc_cluster.root());
Kmeans Cluster

Example:

	var km_cluster = cl.KMean()
	.data([{
		name : '1',
		value : {
			point : [1, 1]
		}
	},
	{
		name : '2',
		value: {
			point : [1.5, 2.0]
		}
	},
	{
		name : '3',
		value: {
			point : [3, 4]
		}
	},
	{
		name : '4',
		value : {
			point : [5, 7]
		}
	},
	{
		name : '5',
		value : {
			point : [3.5, 5]
		}
	},
	{
		name : '6',
		value : {
			point : [4.5, 5]
		}
	},
	{
		name : '7',
		value : {
			point : [3.5, 4.5]
		}
	}
	])
	.clusters([
		{
			'name' : 'C1',
			'value' : {
				'centroid' : [1, 1]
			}
		},
		{
			'name' : 'C2',
			'value' : {
				'centroid' : [5, 7]
			}
		}
	])
	.evaluate_sse(true)
	.save_history(true)
	.stopThreshold(0)
	.accessor(function(d){return d.value.point;})
	.centroid_fun('mean')
	.numIteration(4)
	.dist_metric(cl.euclidean_distance)
	.cluster();
Clustering Evaluation

Example:

	//create some points
	var points = cl.array2points([
			[0.4, 0.53],
			[0.22, 0.38],
			[0.35, 0.32],
			[0.26, 0.19],
			[0.08, 0.41],
			[0.45, 0.30]
		]);

	//perform hierachical clustering
	var cluster = cl.HierachicalCluster()
	.data(points)
	.dist_metric(cl.euclidean_distance)
	.dist_fun('centroid')
	.save_history(true)
	.init()
	.cluster();

	//Cut the hierachical clustering to 3 clusters
	var clustering = cluster.cut_opt('K').cut(3);

	//creating the clustering evaluation object
	var cev = cl.ClusterEvaluation().data(clustering);

	var wss = cev.WSS();
	var bss = cev.BSS();
	var tss = cev.TSS();
	var silhouette = cev
	.silhouette_dist_metric(cl.euclidean_distance)
	.silhouette_coefficient();

Sparse Vector

Example:

	var v1 = cl.SparseVector([0, 1, 5, 6, 10], [1, 1, 1, 1, 1]);
	var v2 = cl.SparseVector([0, 5, 10, 11], [1, 1, 1, 1]);
	var v3 = cl.SparseVector([1], [1]);
	var d = v1.dotp(v2);
	var s = v1.sum(v2);
	console.log('d', d);
	console.log('L2', v1.L2norm());
	console.log('s', s);
	var ind1 = v1.locationAtIndex(5);
	console.log('index1', ind1);
	var ind3 = v3.locationAtIndex(0);
	console.log('index3', ind3);
	console.log('v1', v1.toDenseVector());
	console.log('v2', v2.toDenseVector());
	console.log('v3', v3.toDenseVector());
	console.log('s', s.toDenseVector());

	v1.setValue(1, 2);
	v1.setValue(4, 1);
	v1.setValue(20, 1);
	console.log('v1', v1.toDenseVector());

Dijkstra's shortest path algorithm

Example:

	var nodes = [
	{
		id : 0,
		name : 0
	},
	{
		id: 1,
		name : 1
	},
	{
		id : 2,
		name : 2
	},
	{
		id : 3,
		name : 3
	},
	{
		id : 4,
		name : 4
	},
	{
		id : 5,
		name : 5
	},
	{
		id : 6,
		name : 6
	}
	];

	var edges = [
	{
		source : nodes[0],
		target : nodes[1],
		value : 2
	},
	{
		source : nodes[0],
		target : nodes[2],
		value : 9
	},
	{
		source : nodes[1],
		target : nodes[2],
		value : 4
	},
	{
		source : nodes[1],
		target : nodes[3],
		value : 2
	},
	{
		source : nodes[2],
		target : nodes[3],
		value : 1
	},
	{
		source : nodes[2],
		target : nodes[5],
		value : 3
	},
	{
		source : nodes[2],
		target : nodes[6],
		value : 11
	},
	{
		source : nodes[3],
		target : nodes[4],
		value : 1
	},
	{
		source : nodes[4],
		target : nodes[6],
		value : 7
	},
	{
		source : nodes[5],
		target : nodes[6],
		value : 7
	}
	];

	var G = cl.Graph().nodes(nodes).edges(edges).create();

	var dk = cl.ShortestPathDijkstra()
	.direction('out')
	.init_metric(function(){return 0;})
	.init_source_metric(function(){return Infinity;})
	.comparator(function(a, b){
		return b - a;
	})
	// .source(G.nodes()[0])
	.graph(G);

	var paths = dk();
	var path;
	var i;
	for(i = 0; i < paths.length; i++){
		path = paths[i];
		console.log(i, path.map(function(d){
			return d.id;
		}));
	}

Girvan Newman Network Clustering Althorithm

	var G = cl.Graph().nodes(nodes).edges(edges).create();
	var ge = cl.GirvanNewman().graph(G);
	var tree = ge();
	console.log('tree', tree);

FAQs

Package last updated on 07 Jul 2017

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc