Answer: Unsupervised Learning (Clustering)

## Explanation **Unsupervised Learning (Clustering)** is the correct choice because: - **No pre-labeled data**: The problem states "without having topic labels," which means there are no predefined categories or labels for the news articles - **Grouping similar items**: Clustering algorithms automatically discover patterns and group similar documents together based on their content - **Topic discovery**: This approach identifies natural groupings in the data, which can reveal topics that weren't predefined **Why other options are incorrect:** - **A) Supervised Learning**: Requires labeled training data with known topic categories - **B) Reinforcement Learning**: Focuses on decision-making through trial and error with rewards/penalties, not document grouping - **D) Transfer Learning**: Involves applying knowledge from one domain to another, but still typically requires some labeled data Clustering algorithms like K-means, hierarchical clustering, or topic modeling techniques (LDA) are specifically designed for this type of unsupervised grouping task.

Author: Ritesh Yadav