Implement a Clustering Algorithm | Dataford Interview Questions

Implement a clustering algorithm (some helper methods could be assumed to be implemented). Asked in the ML Coding stage. Loop round for Sr Delivery Consultant GenAI/ML; the interviewer did not require perfectly executing code.

Input and Output

Implement k_means(points, k, max_iterations). points is a non-empty list of equal-length numeric lists. Initialize centroids from the first k points, assign each point to its nearest centroid using squared Euclidean distance, and recompute each centroid as the coordinate-wise mean. Break when centroids stop changing or after max_iterations. If initial centroids contain duplicates, return them unchanged. Return a list of centroid lists in centroid-index order. Ties choose the lowest index.

Constraints

1 <= k <= len(points) <= 1000
All points have the same positive dimension
Point coordinates are finite numbers
max_iterations >= 0
Ties are assigned to the lowest centroid index

Function Signature

def k_means(points, k, max_iterations):

Your question is Implement a Clustering Algorithm. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Input and Output

Constraints

1 <= k <= len(points) <= 1000
All points have the same positive dimension
Point coordinates are finite numbers
max_iterations >= 0
Ties are assigned to the lowest centroid index

Function Signature

def k_means(points, k, max_iterations):

Your question is Implement a Clustering Algorithm. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.