
Department of
Computational
Perception


Special Topics: Exploratory Data
Analysis
344.044,
KV, 2h (3 ETCS), Winter Term 2016/17
Assoc.Prof.
Dr. Markus Schedl
Time: Tuesday, 13:45 
15:15
Start: October 11, 2016
Location: S2 054
The lecture will be held in English.
Goals
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
nonlinear techniques for highdimensional
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),
Multidimensional Scaling (MDS), Sammon's Mapping, various fitness
functions
 SelfOrganizing Maps: SOM, GHSOM, Aligned SOM
 Visualizing SOMs: SOM grid, Music Description Map
(MDM), Bar Plots, Chernoff's Faces, UMatrix, Distance Matrix, Smoothed
Data Histogram (SDH), Component Planes
 Graphbased Methods: Locally Linear Embedding (LLE),
Isomap, Laplacian Eigenmaps
 Probabilistic Models: Stochastic Neighbor Embedding (SNE)
and tDistributed Stochastic Neighbor Embedding (tSNE)
 Information Visualization: TreeMaps, InterRing/Sunburst,
Hyperbolic Browser, TimeSeries Visualization
Schedule
The detailed schedule can be found on the course's KUSSS web page.
last edited by ms on Sep 6, 2016