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Learn how Apriori, FP‑Growth, and Eclat uncover frequent itemsets in massive transactional datasets. A clear and intuitive guide to pruning, compression, and vertical intersections, with practical examples for market basket analysis and association rule mining.
Learn how Association Rules uncover hidden co‑occurrence patterns in transactional data. A clear and intuitive guide to support, confidence, lift, and the Apriori and FP‑Growth algorithms, with practical examples for retail, e‑commerce, and recommendation systems.
Learn how Mean‑Shift and Agglomerative clustering work. Mean‑Shift finds natural density peaks without choosing K, while Agglomerative builds hierarchical dendrograms that reveal every possible segmentation. A clear, intuitive guide to bandwidth, cut distance, and when to use each algorithm
Learn how DBSCAN works, the density-based clustering algorithm that doesn’t require choosing K and naturally detects outliers. A clear, intuitive and narrative explanation of core points, border points, noise, eps, and min_samples.
Learn how K‑means clustering works, how to choose the optimal number of clusters using the Elbow method, and how to validate cluster quality with the Silhouette score. A clear, intuitive and practical guide to unsupervised learning and real‑world segmentation.
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