A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem. Which ONE of the following options represents the BEST possible solution for this above-mentioned task? SELECT ONE OPTION

The problem requires identifying naturally occurring groups of customers with similar tastes without any predefined labels for these groups. This is a classic unsupervised learning problem. Clustering is the specific ML technique designed for this task. It works by partitioning a dataset into distinct groups (clusters) where individuals within a group are more similar to each other than to those in other groups. This directly addresses the company's need to discover customer segments for targeted marketing.

A. Regression is a supervised learning method used to predict a continuous numerical output (e.g., sales amount), not to form groups.
B. Association rule learning finds relationships between items (e.g., products bought together) but does not segment the customers themselves into groups.
D. Classification is a supervised learning method that assigns data points to predefined categories, but here the groups are unknown and need to be discovered.

1. ISTQB® (2021). Certified Tester AI Testing (CT-AI) Syllabus Version 1.0. Section 2.2.2, p. 15. (States that "Clustering is used to group data with similar characteristics" and provides "customer segmentation" as a key example).

2. Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer. Chapter 6: "Cluster Analysis: Basic Concepts and Algorithms," p. 171. (Defines cluster analysis as the task of grouping a set of objects in such a way that objects in the same group are more similar to each other).