K Means Clustering
K means clustering visualization
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.
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
OR
Clone, go to the root directory
npm i
npm run build
npm run start
