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 · This clustering approach assumes data is composed of probabilistic distributions, such as Gaussian distributions. In Figure 3, the distribution-based algorithm clusters data into …

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 …

Aug 25, 2025 · For each cluster, calculate a new centroid by taking the mean position of all points in the cluster. The arrows in Figure 4 show the change in centroid positions.

Aug 25, 2025 · However, there are heuristics and best practices that you can iteratively apply to improve the quality of your clustering. The following flowchart gives an overview of how to …

Le clustering basé sur le centroïde organise les données en clusters non hiérarchiques. Les algorithmes de clustering basés sur le centroïde sont efficaces, mais sensibles aux conditions …

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 …

Nov 17, 2025 · There are two types of hierarchical clustering algorithms: Agglomerative clustering first assigns every example to its own cluster, and iteratively merges the closest clusters to …

Aug 25, 2025 · In clustering, you calculate the similarity between two examples by combining all the feature data for those examples into a numeric value. This requires the features to have …

Aug 25, 2025 · This course doesn't cover how to generalize k-means, but those interested should see Clustering – k-means Gaussian mixture models by Carlos Guestrin from Carnegie Mellon …