K-Means Implementation in R

Implement the K-means clustering algorithm using R programming language in this comprehensive 21-minute tutorial. Gain hands-on experience with one of the most popular unsupervised machine learning techniques for data segmentation and pattern recognition. Learn how to preprocess data, initialize cluster centroids, assign data points to clusters, and update centroids iteratively. Explore practical applications of K-means in various domains, including customer segmentation, image compression, and anomaly detection. Master the intricacies of the algorithm, including handling convergence criteria and dealing with potential limitations. By the end of this tutorial, acquire the skills to effectively apply K-means clustering to your own datasets and extract meaningful insights from unlabeled data.