K-Means Clustering
K-means clustering is an unsupervised learning algorithm that groups similar data points around k centroids without requiring labeled examples.
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K-means clustering is an unsupervised learning algorithm that groups similar data points around k centroids without requiring labeled examples.
Clustering groups unlabeled data points so examples within a group are more similar than examples in other groups.
An autoencoder is a neural network that compresses data into a latent representation and reconstructs it for denoising, learning, or anomaly detection.
A method where AI models identify patterns in unlabeled data without predefined outputs. It’s used in clustering, anomaly detection, and exploratory data analysis.