EpiVECS allows users to perform cluster embedding on the web using a variety of techniques. Cluster embedding is an unsupervised learning technique that combines the strengths of clustering and dimensionality reduction. A cluster embedding method takes a dataset of high-dimensional vectors, assigns each of those vectors to a cluster, and provides a low-dimensional representation of the clusters.

We recommend readers check out the introductory Observable Notebook, which explains the concept of cluster embedding in more detail and shows how to use the EpiVECS JavaScript modules.

Check out the web-tool at: https://episphere.github.io/epivecs/

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EpiVECS is also available as a collection of several ES6 modules:

• @epivecs/cluster_embedding - For the main cluster embedding functionality:

import * as clusterEmbedding from "https://cdn.jsdelivr.net/npm/@epivecs/cluster_embedding/+esm"

• @epivecs/processing - For some of the basic vector processing functionality included in the tool (e.g. smoothing, normalization)

import * as vectorProcessing from "https://cdn.jsdelivr.net/npm/@epivecs/processing/+esm"

• @epivecs/visualization - To help with the visualization of the results.

import * as vectorProcessing from "https://cdn.jsdelivr.net/npm/@epivecs/processing/+esm"

For more details on the operation of these libraries, see the introductory Observable Notebook.