Cluster Analysis: Methods and Applications

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Related lectures (32)

Page 3 of 4

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Graph Coloring II

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Clustering: Theory and Practice

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

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Machine Learning: Fundamentals and Applications

Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.

Clustering: Hierarchical and K-means Methods

Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Kernel K-means: Analysis and Applications

Explores Kernel K-means algorithm, its analysis, applications, and limitations in clustering.

Graph metrics: Statistical analysis

Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.

Characterisation of Clusters: Homogeneity, Separability

Explores centroid, medoid, homogeneity, separability in clustering, quality evaluation, stability, expert knowledge, and clustering algorithms.