Here you’ll find everything about k-means, including Python tutorials that explain implementing k-means for cluster analysis.
K-means is a clustering algorithm that is used to partition a dataset into a specified number of clusters. The algorithm works by defining a distance metric, such as Euclidean distance, and then iteratively assigning each data point to the cluster with the nearest centroid (cluster center). The centroids are then recomputed based on the new assignments, and the process is repeated until the assignments stop changing. The number of clusters, k, is a hyperparameter of the algorithm and must be specified by the user. The optimal value of k can be determined using techniques such as the elbow method or the silhouette coefficient. Once the clusters have been identified, they can be used for a variety of purposes, such as identifying patterns in the data or performing further analysis on each cluster. K-means is a simple and widely used algorithm for clustering data.
Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more
