Topics Learnt Today:
K-medoids:
- Strengths:
K-medoids, also known as PAM (Partitioning Around Medoids), is a more robust alternative to K-means. It identifies clusters based on representative points called medoids. These medoids are actual data points within the dataset, making them more suitable for handling irregularly shaped clusters.
Unlike K-means, K-medoids is less sensitive to outliers, making it a better choice when dealing with data containing noise or extreme values. - Weaknesses:
While K-medoids is more versatile than K-means in terms of cluster shape, it can still struggle with very large datasets due to its computational complexity.