Understanding K-means Clustering in Machine Learning

K-means clustering is an unsupervised machine learning algorithm that groups objects based on attributes. It is widely used in various industries, such as customer segmentation, recommendation engines, and similarity search. The algorithm works by calculating the distance of each data element from the geometric center of a cluster and reconfiguring the cluster if it finds a point belonging to a specific cluster closer to the centroid of another cluster. K-means clustering is useful in areas such as image processing, information retrieval, recommendation engines, and data compression. The number of clusters can be chosen using methods like the elbow method or the silhouette method. Zilliz offers a one-stop solution for challenges in handling unstructured data, especially for enterprises that build AI/ML applications that leverage vector similarity search.