npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

hclust

v1.0.2

Published

Agglomerative hierarchical clustering for Node JS

Downloads

26

Readme

hclust.js - Agglomerative hierarchical clustering for Node JS

Description

Agglomerative hierarchical clustering for Node JS (no dependencies).

Parameters

Method can be called by hclust(arrays, distanceType = 'euclidean', linkageType = 'average', na = 'pairwise', arrayMin, arrayMax), where

  • arrays - list of arrays to calculate clusters

  • distanceType - distance measures

    • euclidean (default) - Eucledian distance, usual distance between the two vectors
    • maximum - maximum distance between two components of x and y
    • canberra - sum(|x_i - y_i| / |x_i + y_i|)
    • manhattan - absolute distance between the two vectors
    • percent - percent of similarity between vectors, based on possible maximum difference between vectors
    • cosine - cosine similarity
    • angular - angular similarity
    • pearson - Pearson correlation based distance
    • spearman - Spearman correlation based distance
  • linkageType - linkage method

    • single - Method of single linkage or nearest neighbour. Proximity between two clusters is the proximity between their two closest objects.
    • complete - Method of complete linkage or farthest neighbour. Proximity between two clusters is the proximity between their two most distant objects.
    • average (default) - Simple average, or method of equilibrious between-group average linkage (WPGMA) is the modified previous. Proximity between two clusters is the arithmetic mean of all the proximities between the objects of one, on one side, and the objects of the other, on the other side; while the subclusters of which each of these two clusters were merged recently have equalized influence on that proximity – even if the subclusters differed in the number of objects.
  • na - handling missing data (they must be null)

    • pairwise (default) - pairwise deletion of missing values
  • arrayMin, arrayMax - range or your variable for calculation of percent difference, otherwise their values will be found in data

Installation

npm install hclust --save

Usage

const hclust = require('hclust')

let arrays = [[5,2,1,4,1,6,2], [1,5,3,5,5,6,1], [6,2,7,7,5,6,7], [1,2,3,4,6,6,7]]
let clusters = hclust(arrays)
console.log(clusters)

Expected outcome:

Alternatives

hcluster.js - Agglomerative Hierarchical Clustering in JavaScript. (that plays nice with d3.js).

ml-hclust - Hierarchical clustering algorithms in JavaScript (includes divisive analysis).

License

MIT