Summary  
K-means is an unsupervised clustering algorithm that partitions data into a predefined number of clusters by iteratively assigning points to the nearest centroid and updating those centroids to minimize intra-cluster distances and maximize inter-cluster separation.

General domain of usage  
Customer segmentation

Among clustering algorithms, **K-means** is a widely popular and effective method. It partitions data into K distinct clusters, where K is a **pre-defined number**. 

The goal of K-means is to **minimize distances within clusters** and **maximize distances between clusters**. This creates internally similar and externally distinct groups. **K-means** has numerous applications, such as: 

-   **Customer segmentation**: grouping customers for targeted marketing; 
-  **Document clustering**: organizing documents by topic;   
-  **Image segmentation**: dividing images for object recognition; 
-   **Anomaly detection**: identifying unusual data points. 

Which statement best describes the main concept of K-means clustering

Unlock the power of cluster analysis in Python. Group unlabeled data using K-Means, Hierarchical Clustering, and DBSCAN. Visualize clusters, interpret dendrograms, and evaluate results with Silhouette Score to reveal hidden patterns in your datasets.

What is K-Means Clustering?

What is K-Means Clustering?