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 · 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 · 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 · 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.

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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 · 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 …

Sep 12, 2025 · Clustering groups similar data points together, often used in unsupervised learning where data lacks labels. Clustering algorithms can be categorized into centroid-based (like k …

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 …

Sep 17, 2025 · Clustering differs from classification because the categories aren't defined by you. For example, an unsupervised model might cluster a weather dataset based on temperature, …