K-Means Clustering

K-Means Clustering is a popular unsupervised machine learning algorithm used to partition data into k distinct groups based on similarity. It works by assigning each data point to the nearest cluster centroid and then updating those centroids iteratively to minimize the within-cluster variation. In practical use cases like YouTube analytics, it can group videos based on engagement patterns or viewer behavior, uncovering hidden structures in the data without needing labeled outcomes.

Assumptions

• Clusters are spherical and roughly equal in size
• Distance metric (typically Euclidean) captures meaningful similarity
• Number of clusters k is known or chosen using evaluation methods

Use Cases

• Customer segmentation based on behavior or preferences
• Grouping content (e.g., YouTube videos) based on performance metrics
• Detecting outliers or unusual patterns in datasets
• Market basket analysis and recommendation systems

Steps

1. Choose the number of clusters k.
2. Randomly initialize k centroids.
3. Assign each point to the nearest centroid (using a distance metric).
4. Recalculate each centroid as the mean of its assigned points.
5. Repeat the assign-update steps until convergence (no changes or minimal change).

Evaluation & Interpretation

K-Means aims to minimize the total **within-cluster sum of squares** (WCSS), also known as **inertia**. The lower the WCSS, the more cohesive the clusters. However, K-Means is sensitive to initial centroid placement, so it’s common to run the algorithm multiple times or use the **k-means++** initialization.

To determine the best number of clusters, you can use methods like the **elbow method**, **silhouette score**, or **gap statistic**.