K-Means is one of the simplest and most popular machine learning algorithms. It belongs to unsupervised learning methods, meaning it does not use labeled data. This algorithm automatically detects patterns and groups data into clusters based on similarity.
How K-Means Works
The process starts by initializing centroids, which serve as representative points for each cluster. Each data point moves to the nearest centroid based on distance. Then, the algorithm recalculates centroids by averaging all points within a cluster. This cycle repeats until centroid positions stabilize.
Applications of K-Means
K-Means finds applications in various fields. Marketers use it for customer segmentation, while healthcare professionals classify patients based on medical data. In image analysis, it helps recognize patterns and categorize objects. Additionally, businesses apply it to anomaly detection, identifying unusual behaviors in datasets.
Advantages of K-Means
K-Means offers several benefits. It processes large datasets quickly and efficiently. Its simple implementation delivers clear results. However, its sensitivity to initial centroid selection can impact final cluster distribution.