ML Knowledge

What is k-means clustering? Does it find a global optimum? Implement k-means clustering, with inputs an N*D matrix and outputs k N_i*D matrices.

Data Scientist

Amazon

Flexport

Udacity

Workday

Confluent

Intuit

Did you come across this question in an interview?

  • What is k-means clustering? Does it find a global optimum? Implement k-means clustering, with inputs an N*D matrix and outputs k N_i*D matrices.
  • Can you explain the process of k-means clustering and whether it assures a global optimum solution? Additionally, demonstrate how you would implement this with an N*D matrix.
  • What does k-means clustering entail, and does it always result in a global optimum? Write a program for k-means clustering given an N*D matrix.
  • How does k-means clustering work, and can it guarantee finding the global optimum? Create an implementation for k-means with an N*D input matrix.
  • Could you detail the k-means clustering algorithm and its ability to find a global optimum? Moreover, implement this algorithm for an N*D matrix input.
  • What is the principle behind k-means clustering, and does it locate the global optimum? Code up an implementation that takes an N*D matrix.
  • How can you explain k-means clustering and its effectiveness in finding a global optimum? Provide an implementation for an N*D matrix input.
  • What is involved in k-means clustering, and is a global optimum achievable? Develop an implementation for this algorithm using an N*D matrix.
  • How would you define k-means clustering and its likelihood of achieving a global optimum? Proceed to implement it for an N*D matrix data set.
  • How would you describe k-means clustering, and is it capable of reaching a global optimum? Also, code a k-means clustering function for an N*D matrix.

Interview question asked to Data Scientists interviewing at Addepar, Nubank, Wish and others: What is k-means clustering? Does it find a global optimum? Implement k-means clustering, with inputs an N*D matrix and outputs k N_i*D matrices..