Aug 25, 2025 · Clustering is an unsupervised machine learning technique designed to group unlabeled examples based on their similarity to each other. (If the examples are labeled, this …

Aug 25, 2025 · Centroid-based clustering organizes the data into non-hierarchical clusters. Centroid-based clustering algorithms are efficient but sensitive to initial conditions and outliers.

Aug 25, 2025 · Before a clustering algorithm can group data, it needs to know how similar pairs of examples are. You can quantify the similarity between examples by creating a similarity …

Aug 25, 2025 · Objectives: Describe clustering use cases in machine learning applications. Choose the appropriate similarity measure for an analysis. Cluster data with the k-means …

Sep 17, 2025 · An unsupervised learning model's goal is to identify meaningful patterns among the data. In other words, the model has no hints on how to categorize each piece of data, but …

Aug 25, 2025 · Since clustering outputs are often used in downstream ML systems, see if downstream model performance improves when your clustering process changes. This offers …

Aug 25, 2025 · This course focuses on k-means because it scales as O (n k), where k is the number of clusters chosen by the user. This algorithm groups points into k clusters by …

Aug 25, 2025 · A straightforward implementation of k-means can struggle with clusters of different densities and sizes. The left side of Figure 1 shows the clusters we'd expect to see, while the …

Aug 25, 2025 · This section reviews the data preparation steps most relevant to clustering from the Working with numerical data module in Machine Learning Crash Course. In clustering, you …

Clustering Clustering is a key unsupervised machine learning strategy to associate related items.