K-Means Clustering in Machine Learning
K-means is a data clustering approach for unsupervised machine learning that can separate unlabeled data into a predetermined number of disjoint groups of equal variance – clusters – based on their similarities. It’s a popular algorithm thanks to its ease of use and speed on large datasets. In this blog post, we look at its underlying principles, use cases, as well as benefits and limitations. What is k-means clustering? K-means is an iterative algorithm that splits a dataset […]