K-Means Clustering Use Cases

K-Means Clustering

Purpose: K-means is an unsupervised learning algorithm used for clustering data into a predefined number of clusters (k).

How It Works:

1. Initialization: Select k initial cluster centroids randomly.
2. Assignment: Assign each data point to the nearest cluster centroid.
3. Update: Recalculate the centroids of the clusters based on the assigned data points.
4. Iterate: Repeat the assignment and update steps until convergence (centroids no longer change significantly).

Use Cases:

• Customer Segmentation: Grouping customers based on purchasing behavior to identify distinct customer segments.
• Image Compression: Reducing the number of colors in an image by clustering similar colors together.
• Anomaly Detection: Identifying outliers as points that do not fit well into any cluster.
• Market Segmentation: Dividing a market into distinct groups of consumers with similar needs or characteristics.