Which of the following is an example of unsupervised learning?

Which of the following is an example of unsupervised learning?

• A. Image Classification
• B. Spam Detection
• C. K-means Clustering
• D. Time Series Forecasting

Answer: (C)

Unsupervised learning is a type of machine learning where the model is trained on data without labeled outputs. This allows the algorithm to identify patterns and structures within the data autonomously, without guidance regarding the expected outcomes. K-means clustering is a prime example of unsupervised learning as it groups data points into clusters based solely on their features. The algorithm identifies centroids representing the center of each cluster, and then allocates data points to the nearest centroid. This process enables the discovery of natural groupings within the data. Since there are no predefined labels or categories for the algorithm to learn from, it exemplifies the foundational principles of unsupervised learning. In contrast, image classification and spam detection are supervised learning tasks, where the model learns from labeled datasets—images are classified into predefined categories, and spam detection models are trained on labeled emails that indicate whether each email is spam or not. Time series forecasting typically involves using historical data to predict future values, which often requires labeled or structured time-dependent inputs, fitting more within the supervised learning framework.

Unsupervised learning is a type of machine learning where the model is trained on data without labeled outputs. This allows the algorithm to identify patterns and structures within the data autonomously, without guidance regarding the expected outcomes.

K-means clustering is a prime example of unsupervised learning as it groups data points into clusters based solely on their features. The algorithm identifies centroids representing the center of each cluster, and then allocates data points to the nearest centroid. This process enables the discovery of natural groupings within the data. Since there are no predefined labels or categories for the algorithm to learn from, it exemplifies the foundational principles of unsupervised learning.

In contrast, image classification and spam detection are supervised learning tasks, where the model learns from labeled datasets—images are classified into predefined categories, and spam detection models are trained on labeled emails that indicate whether each email is spam or not. Time series forecasting typically involves using historical data to predict future values, which often requires labeled or structured time-dependent inputs, fitting more within the supervised learning framework.