Explanation

Unsupervised Learning (Clustering) is the correct choice because:

1. No pre-existing labels: The problem states you want to group articles "without having topic labels." This is a classic unsupervised learning scenario where you don't have labeled training data.

2. Clustering purpose: Clustering algorithms automatically group similar data points together based on their features. In this case, the algorithm would analyze the content of news articles and group similar ones into topics.

3. Why not other options:

   • Supervised Learning (A): Requires labeled training data with known topic labels.
   • Reinforcement Learning (B): Involves an agent learning through rewards/punishments in an environment, not suitable for grouping documents.
   • Transfer Learning (D): Involves using knowledge from one task to help with another related task, but still typically requires some labeled data.

4. Real-world application: This is exactly what clustering algorithms like K-means, hierarchical clustering, or DBSCAN are designed for - discovering inherent groupings in unlabeled data.

Key takeaway: When you need to find patterns or groupings in data without any pre-existing labels, unsupervised learning (specifically clustering) is the appropriate machine learning approach.