Cl 3 - Clustering (32 min)
Channel: Prof. Laurenz Wiskott
Date: 2017-01-26

Clustering - Sec. 3 (32 min)


LECTURE VIDEO 3 of the

Lecture Notes on Clustering

Prof. Laurenz Wiskott
Institut für Neuroinformatik
Ruhr-Universität Bochum, Germany, EU

Data often has structure that one wants to discover.  One type of structure is
clustering, i.e. data points tend to concentrate in certain regions.  Clustering
algorithms are designed to discover such concentration regions.

==================================================================== LECTURE   [80 min]
---------------------------------------------------------------------- VIDEO 1 (8 min, on YouTube)
1 Introduction
---------------------------------------------------------------------- VIDEO 2 (14 min, on YouTube)
2 Hard partitional clustering
2.1 K-means algorithm
2.2 Davies-Bouldin index
---------------------------------------------------------------------- VIDEO 3 (32 min, on YouTube)
3 Soft partitional clustering
3.1 Gaussian mixture model
3.1.1 Introduction
3.1.2 Isotropic Gaussians
3.1.3 Maximum likelihood estimation
3.1.4 Conditions for a local optimum
3.1.5 EM algorithm
3.1.6 Practical problems
3.1.7 Unisotropic Gaussians +
3.2 Partition coefficient index
---------------------------------------------------------------------- VIDEO 4 (19 min, on YouTube)
4 Agglomerative hierarchical clustering
4.1 Dendrograms
4.2 The hierarchical clustering algorithm
4.3 Validating hierarchical clustering
---------------------------------------------------------------------- VIDEO 5 (7 min, for my students only)
5 Applications
----------------------------------------------------------------------
====================================================================

More material is available at
https://www.ini.rub.de/PEOPLE/wiskott/Teaching/Material/index.html