Department of Computational Perception

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Spezielle Kapitel aus Informatik: Exploratory Data Analysis

344.031, KV, 2h, WS 2012/13

Univ.Ass. Dr. Markus Schedl

TIME: Thursday, 13:45 - 15:15
START: Thursday, 18.10.2012
PLACE: S2 044

The lecture will be held in English.

Goals

This lecture gives an introduction to techniques and applications used in Visual Data Mining and methods underlying Exploratory Data Analysis. Special emphasis is given to 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 certain techniques in a pratical exercise.

The main topics covered will likely include:

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: Generative Topographic Mapping (GTM), Hierarchical GTM, t-Distributed Stochastic Neighbor Embediing (tSNE)
Random Projections, Locality Sensitive Hashing (LSH)
Information Visualization: TreeMaps, InterRing/Sunburst, Hyperbolic Browser, Time-Series Visualization

Schedule

18.10. Introduction to Exploratory Data Analysis and Visual Data Mining
25.10. Principles and General Foundation of EDA and Data Projection Techniques, Similarity Measurement
08.11. PCA, MDS, Sammon's Mapping
15.11. SOM: Basic Principles and Concepts
22.11. SOM: Extensions, Training, GHSOM, Aligned SOMs
29.11. Visualization of SOMs: SOM-grid, MDM, Component Planes, U-Matrix, Distance Matrix, SDH, Bar Plots, Chernoff's Faces
06.12. Graph-based Methods: LLE, Isomap, Laplacian Eigenmaps
13.12. Probabilistic Methods: GTM, Hierarchical GTM, t-Distributed Stochastic Neighbor Embediing (tSNE)
10.01. Random Projections, Locality Sensitive Hashing (LSH), Basics in InfoVis
17.01. InfoVis ctd.
24.01. Student's Presentations of Practical Exercise
31.01. Written Examination

last edited by pk on Sep 10, 2012