Clustering Techniques: K-means and DBSCAN

Related lectures (32)

Page 3 of 4

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

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

Introduces the kernel k-means method to form non-convex clusters and discusses clustering by density to identify dense regions in datasets.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Compares K-Means and Spectral Clustering algorithms, highlighting their differences and practical applications in clustering student behaviors.

Explores single cell RNA-sequencing methods, applications, tissue studies, and data clustering.

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

Introduces Bayesian Knowledge Tracing, Additive Factors Model, and clustering algorithms for tracing student knowledge and discovering structures.