K-Means-Clustering (EN)

K-Means-Clustering Algorithm

K-Means is an unsupervised learning algorithm that divides data points into k clusters, with each data point assigned to the cluster with the nearest center (centroid). The algorithm minimizes the sum of squared deviations between data points and their cluster center. It is particularly effective for large datasets and is frequently used in segmentation, grouping, and compression applications.

Algorithm Process

flowchart TD     A[Data points] --> B[Random initialization of k centroids]     B --> C[Assignment of each data point to the nearest centroid]     C --> D[Calculation of new centroids as mean of assigned points]     D --> E{Convergence reached?}     E -->|No| C     E -->|Yes| F[Final clusters] 

In Context

• Typically used with: Elbow method, Silhouette analysis
• Related to: Hierarchical clustering, DBSCAN, GMM
• Example use: Customer segmentation, image compression, grouping similar documents