[Verse 1]
Started with a dataset, scattered points everywhere
No pattern visible, just chaos in the air
Need to find the groups hidden in this mess
K-means clustering gonna clean up this stress
Choose your K value, how many clusters you want
Initialize centroids, random placement to start
Each point gets assigned to the nearest center mass
Distance calculations, Euclidean math class
[Chorus]
Find the center, move the center, iterate and repeat
Until convergence, no more movement, algorithm complete
K-means clustering, grouping data neat
Minimize the variance, make those clusters sweet
Find the center, move the center, that's the way we roll
Unsupervised learning, reaching for the goal
[Verse 2]
Assignment step first, measure every distance
Point to centroid, choose the least resistance
Update step comes next, recalculate the mean
New centroid position where the cluster's been
Iterate the process, watch the centers drift
Objective function dropping, that's the algorithm's gift
Within-cluster sum of squares, that's what we minimize
Keep on iterating till the movement dies
[Chorus]
Find the center, move the center, iterate and repeat
Until convergence, no more movement, algorithm complete
K-means clustering, grouping data neat
Minimize the variance, make those clusters sweet
Find the center, move the center, that's the way we roll
Unsupervised learning, reaching for the goal
[Bridge]
Lloyd's algorithm, that's the formal name
Elbow method helps you choose K for the game
Watch out for local minima, might get trapped inside
Random initialization, give it multiple tries
Spherical clusters work the best with this approach
Non-convex shapes might need a different coach
[Verse 3]
Convergence criteria, when do we stop the flow
When centroids don't move or movement's really slow
Computational complexity, linear in your size
Big O of N times K times iterations, analyze
Applications everywhere, customer segmentation
Image compression, market basket revelation
Simple yet powerful, easy to implement
K-means clustering, time and resources well spent
[Chorus]
Find the center, move the center, iterate and repeat
Until convergence, no more movement, algorithm complete
K-means clustering, grouping data neat
Minimize the variance, make those clusters sweet
Find the center, move the center, that's the way we roll
Unsupervised learning, reaching for the goal
[Outro]
From chaos to order, patterns now revealed
K-means clustering, data science sealed
Remember the steps, assignment then update
Keep iterating till convergence, don't be late