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Clustering Techniques: K-means and DBSCAN
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Related lectures (33)
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
Clustering: Hierarchical and K-means Methods
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Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.
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Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
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Unsupervised Learning: Dimensionality Reduction and Clustering
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Introduction to Clustering: Methods and Applications
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Covers the fundamentals of clustering in unsupervised learning and its practical applications.
K-means Algorithm
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Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.
Characterisation of Clusters: Homogeneity, Separability
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Explores centroid, medoid, homogeneity, separability in clustering, quality evaluation, stability, expert knowledge, and clustering algorithms.
Kernel K-Means Method
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Introduces the kernel k-means method to form non-convex clusters and discusses clustering by density to identify dense regions in datasets.
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Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Clustering & Density Estimation
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