Entity Resolution: Techniques and Applications

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Related lectures (32)

Page 3 of 4

Temporality and Entity Resolution

Explores challenges in data temporality and techniques for entity resolution.

Clustering: K-means & LDA

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

Clustering: K-Means

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

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

File Organization and Indexing

Explores file organization, record formats, indexing techniques, and index classifications in databases, emphasizing the importance of efficient data storage and access.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Kernel K-means: Analysis and Applications

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

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++.

Dimensionality Reduction: PCA and LDA

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

Nearest Neighbor Rules: Part 2

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