Cluster Explorer — WhizzStep AI Lab

How K-Means Clustering Works

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1. Place Centroids

Pick K random "centroid" points — one for each cluster you want to find.

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2. Assign Points

Colour every data point by its nearest centroid — that's its cluster!

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3. Move Centroids

Shift each centroid to the average position of all points in its cluster.

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4. Repeat Until Still

Keep assigning & moving until centroids stop moving — that's convergence!

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Wizzy the AI Tutor

Welcome to Cluster Explorer! 🎉 K-Means finds hidden groups in data — without any labels! First, let's create a dataset. Click or drag on the canvas to drop data points. Or pick a preset dataset. Can you create 3 clear groups?

Step 1 — Create Your Dataset

0 points

Presets:

💡 Click anywhere to add a point · Click & drag to paint multiple

📊 Dataset Info

Total points0

Canvas area480 × 400

Point size

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Wizzy the AI Tutor

Now choose K — the number of clusters! Look at your data and guess how many groups there are. Then you can click on the canvas to manually place your starting centroids, or let the algorithm pick them randomly!

Step 2 — Choose K & Place Centroids

💡 Click to place K centroid(s) — or use Random Init

Number of clusters (K)

🎯 Centroid Setup

K (clusters)3

Placed0 / 3

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Wizzy the AI Tutor

Watch the magic! ✨ Click "Step" to see each assignment and centroid-move separately, or "Auto Run" to watch it converge automatically. The ★ stars are centroids — watch them drift toward the centre of their clusters!

Step 3 — K-Means in Action

Iterations

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Current Phase

Ready

Status

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Inertia (WCSS)

Press Step to begin the first assignment phase, or Auto Run to watch the full algorithm!

📊 Clustering Stats

Iteration0

Total points—

Inertia (WCSS)—

Centroid shift—

Converged?No

Animation speed

📉 Inertia over Iterations

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Wizzy the AI Tutor

But how do you choose the right K? The Elbow Method runs K-Means for K=1 to 8 and plots the inertia. The "elbow" bend is where adding more clusters stops helping much. Can you spot the elbow in your data?

Step 4 — The Elbow Method

🔍 Elbow Analysis

Best K (suggested)—

Your chosen K—

Data points—

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Wizzy the AI Tutor

🎊 You've just run the same algorithm used in Spotify playlist grouping, customer segmentation, image compression, and medical diagnosis! K-Means is one of the most powerful unsupervised learning tools. You are now a Clustering Scientist! 🌟

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Clustering Scientist Badge!

You ran K-Means clustering and used the Elbow Method. Enter your name for your certificate!

🔵 WhizzStep AI Lab

This certifies that

Student Name

has mastered K-Means Clustering & Unsupervised Learning

K-Means Master

Elbow Expert

Cluster Scientist

Clustered — points · K=— · whizzstep.in

Key Concepts You've Mastered

Unsupervised Learning

🔓 No Labels Needed

Unlike classification, clustering finds patterns in data without any predefined categories or labels.

Centroid

⭐ The Cluster Centre

A centroid is the mathematical average position of all points in a cluster. It moves each iteration.

Inertia (WCSS)

📏 Cluster Tightness

Within-Cluster Sum of Squares. Measures how spread out points are within each cluster.

Elbow Method

💪 Choosing K

Plot inertia for K=1..8. The "elbow" bend shows where adding more clusters gives diminishing returns.

Convergence

🛑 When It Stops

K-Means converges when centroids stop moving — the clusters have stabilised into a final answer.

Real World Uses

🌍 Where It's Used

Spotify playlists, customer groups, image compression, medical diagnosis, news grouping, and more!