Graph Mining: Social Networks and Data Analysis

Related lectures (32)

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Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.