K-Means Clustering
K-Means is an unsupervised clustering algorithm that partitions data into K groups by minimizing the distance between points and their assigned cluster centroids. It is one of the most widely used clustering methods.
Understanding K-Means Clustering
K-means clustering is one of the most widely used unsupervised learning algorithms, partitioning data into k distinct groups by iteratively assigning points to the nearest cluster centroid and then updating centroids to the mean of assigned points. The algorithm is valued for its simplicity, speed, and scalability, making it practical for large datasets in customer segmentation, document grouping, and image compression. However, k-means requires specifying the number of clusters in advance and assumes roughly spherical, equally sized clusters, which limits its effectiveness on complex data distributions. Techniques like the elbow method and silhouette analysis help determine the optimal number of clusters. Variants such as k-means++ improve initialization, while mini-batch k-means accelerates training on large datasets. For non-convex cluster shapes, alternatives like hierarchical clustering or DBSCAN may be more appropriate.
Category
Machine Learning
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