MLGrind - Master Machine Learning

Why this matters:

Tests understanding of iterative optimization and clustering – frequently asked for recommendation systems.

K-Means partitions data into k clusters by iteratively assigning points to the nearest centroid and updating centroids to be the mean of assigned points.

Your implementation should:

Initialize centroids randomly

Assign points to nearest centroid

Update centroids

Repeat until convergence

Examples

Input:

X = [[1,1], [1,2], [5,5], [5,6]], k=2

Output:

labels = [0, 0, 1, 1]

Explanation:

Points are clustered into 2 groups

Constraints

Use Euclidean distance
Maximum 100 iterations
Converge when centroids do not change

K-Means Clustering

Examples

Constraints

Tags

Similar Problems

Output