You work as a machine learning specialist for a marketing consulting firm. Your firm has an online retailer as a client that wants to apply different marketing strategies per segment of their customer base. They have decided that the best way to segment their customers is by their purchase history. You have all of the online retailer purchase histories from the last 5 years that you can use for your machine learning model.
Which type of machine learning algorithm would give you segmentation based on purchase history in the most expeditious manner?

Answer options:

A.K-Nearest Neighbors (KNN)
B.K-Means
C.Semantic Segmentation
D.Neural Topic Model (NTM)

Answer: B
Option A is incorrect. The k-nearest neighbor algorithm is used to find items that are similar to each other. This may find purchases that are similar to each other, but not customers that have similar purchase history. You would have to do additional modeling to use this algorithm.
Option B is correct. The K-Means algorithm is used to find groups within data where the group members are similar to each other but different from members of other groups. This is exactly what you are trying to solve: find groups of customers with similar purchase history.
Option C is incorrect. The semantic segmentation algorithm is used to develop computer vision applications. You are trying to solve a clustering problem. So this algorithm would not work for this problem.
Option D is incorrect. The Neural Topic Model algorithm is used to group documents into topics using the statistical distribution of words within the documents. You are trying to solve a clustering problem. So this algorithm would not work for this problem.
Reference:
Please see the Amazon SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms, and the article titled The 5 Clustering Algorithms Data Scientists Need to Know.