K means clustering visualization

https://user-images.githubusercontent.com/29355962/116796469-d8c6e900-ab06-11eb-9920-cf0d086f51ba.mp4

Created using Svelte and Florence, feel free to use in education purpose. Give me a ⭐ if you like it and want to save for using in the future.

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids.

Wikipedia

Command

==============K-MEANS CLUSTERING==============

help

Show this help

assign

Assign each observation to the cluster with the nearest mean.

update

Recalculate means (centroids) for observations assigned to each cluster.

fit

Repeat assign and update until the assignments no longer change.

set --help

To show how to set variables

reset

Shuffle data again, it will recreate centers of data and random change position of labels

git be email web

Show about me

clear history

Same with terminal

Set

set label n

Set the amount of label (2≤n≤9)

set data n

Set the amount data for each label (n ≥ 10 , should be < 100 for better visualize)

set error n

Set the error range for data from center point (5≤n≤50)

Usage

Demo

OR

Clone, go to the root directory

npm i
npm run build
npm run start