Department of Computational Perception

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Special Topics: Exploratory Data Analysis

344.044, KV, 2h (3.0 ECTS), WS 2014/15

Assoc.-Prof. Dr. Markus Schedl

Time: Tuesday, 13:45-15:15
Start: Tuesday, Oct. 7, 2014
Location: S3 055

This class will be taught in English.

Class Objectives and Content

This lecture 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 will likely 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

07.10. Introduction to Exploratory Data Analysis and Visual Data Mining
14.10. Principles and General Foundation of EDA and Data Projection Techniques, Similarity Measurement
21.10. Spectral Methods: PCA, MDS, Sammon's Mapping
04.11. Self-Organizing Maps (SOM): Basics, Online- and Batch-learning, hierarchical clustering, Aligned-SOM
11.11. Visualization of SOMs: SOM-grid, MDM, Component Planes, U-Matrix, Distance Matrix, SDH, Bar Plots, Chernoff's Faces
18.11. Graph-based Methods: LLE, Isomap, Laplacian Eigenmaps
25.11. Probabilistic Methods: non-probabilistic vs. probabilistic methods, SNE and t-SNE
02.12. Information Visualization
16.12. Q&A for Practical Exercise
13.01. Student's Presentations of Practical Exercise
20.01. Written Examination

last edited by ms on Sep 2, 2014