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Department of
Computational
Perception
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Special Topics: Exploratory Data Analysis
344.044, KV, 2h (3.0 ECTS), WS 2015/16
Assoc.-Prof. Dr.
Markus Schedl
Time: Tuesday,
13:45-15:15
Start: Tuesday, Oct. 6, 2015
Location: S3 055
This class will be taught in English.
Class Objectives and Content
This course gives an introduction to techniques, tools, and
applications used
in Visual Data Mining
and methods underlying Exploratory
Data
Analysis. Special emphasis is given to linear and
non-linear techniques for high-dimensional
data projection, clustering (unsupervised learning), and related
information visualization methods. Students will also be given the
opportunity to implement and test these techniques in a pratical
exercise.
The main topics covered include:
- Basics: motivation for EDA, categorization of EDA
techniques, different kinds of input data, similarity functions
- Spectral Methods: Principal Components Analysis (PCA),
Multi-dimensional Scaling (MDS), Sammon's Mapping, various fitness
functions
- Self-Organizing Maps: SOM, GHSOM, Aligned SOM
- Visualizing SOMs: SOM grid, Music Description Map
(MDM), Bar Plots, Chernoff's Faces, U-Matrix, Distance Matrix, Smoothed
Data Histogram (SDH), Component Planes
- Graph-based Methods: Locally Linear Embedding (LLE),
Isomap, Laplacian Eigenmaps
- Probabilistic Models: Stochastic Neighbor Embedding (SNE)
and t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Information Visualization: TreeMaps, InterRing/Sunburst,
Hyperbolic Browser, Time-Series Visualization
Schedule
The detailed schedule can be found on the course's KUSSS web page.
last edited by ms on Aug 21, 2015