Visual Cluster Analysis

Visual Cluster Analysis

Cluster analysis offers overviews of large data sets, by finding and presenting only a limited number of cluster prototypes to the user. Visualization allows interactive exploration of clustering results. In this project, we develop a visual cluster analysis framework which lets the user monitor and control the overall clustering process by visual-interactive means. Thereby, it is possible to understand why the given results are obtained. We currently consider the Self-Organizing Map algorithm.

Contact: Tatiana von Landesberger, Application Domains: finance, geo and earth observation, natural sciences, biology, etc.

Visual Search and Analysis 44 created by 'mi03niza (Kromminga, Lukas) (Kromminga, Lukas)' on uploading a picture
Visual Cluster Analysis of trajectory data. Clusters are grouped on a regular grid with topological ordering using the Self-organizing Map algorithm.
Visual Search and Analysis 45 created by 'mi03niza (Kromminga, Lukas) (Kromminga, Lukas)' on uploading a picture
Visual Cluster Analysis of spoken letter data. The cluster algoritm arranges phonetically similar letters close together in a map lattice (blue). The cluster result is interactively labeled with yellow sketches.

Data Types

Trajectory Data

Finance: changes of Risk Return values over time

Motion Capturing: analysis of motion behavior over time [Reference: HDM05]

High Dimensional Data

Speech recognition: grouping of spoken letter data elements [Reference: ISOLET]

Univariate Time Series Data

Earth observation: analysis of daily temperature patterns [Reference: PANGAEA]

Bivariate Time Series Data

Earth observation: correlation of measurements of two different physical units [Reference: PANGAEA]

Multivariate Time Series Data

Earth observation: analysis of time-oriented multidimensional measurement data [Reference: PANGAEA]

Motion Capturing: analysis of motion behavior over time [Reference: HDM05]

Sequence Data

Material science: analysis of light-reflectance behaviors

Publications

A Visual Digital Library Approach for Time-Oriented Research Data

J. Bernard, J. Brase, D. Fellner, O. Koepler, J. Kohlhammer, T. Ruppert, T. Schreck, I. Sens

To appear in: Proceedings of European Conference on Research and Advanced Technology for Digital Libraries (ECDL), September 2010

Micro-Macro Views for Visual Trajectory Cluster Analysis

J. Bernard, T. von Landesberber, S. Bremm, T. Schreck

In: IEEE Information Visualization, 2009

Visual cluster analysis of trajectory data with interactive kohonen maps

T. Schreck, J. Bernard, T. Von Landesberger, J. Kohlhammer

In: Information Visualization 8(1) 14 – 29, 2008