Visual Clustering in Parallel Coordinates and Graphs

PhD Thesis Proposal Defence


Title: "Visual Clustering in Parallel Coordinates and Graphs"

by

Miss Hong ZHOU


Abstract:

Information visualization has emerged as a very active research field
for multivariate and relational data analysis in recent years. It
turns complex and abstract data such as demographic data, financial
data, social networks, and paper citations into visual
representations, and then users can exploit interactive computer
graphics techniques and human visual capabilities to gain insight into
the data. Parallel coordinates and graphs are two well-established
methods in information visualization. However, when data become very
large, the effectiveness of both methods is dramatically reduced as
tens of thousands of lines can easily overwhelm the display and the
resulting visual clutter will obscure any underlying patterns. Thus,
clutter reduction for parallel coordinates and graphs is a very
important research problem in information visualization.

In this proposal, we introduce visual clustering as a new approach for
clutter reduction and pattern detection. Compared with traditional
clutter reduction methods such as filtering and brushing, visual
clustering can enhance and reveal interesting patterns in the data
while preserving the context. For parallel coordinates, we present a
force-based optimization method to bundle polylines by adjusting their
shapes, and a splatting framework to reveal features with animations.
For graphs, an energy-based hierarchical visual clustering scheme and
a geometry-based edge grouping approach are proposed. The
effectiveness of these methods has been demonstrated through extensive
experiments using both synthetic data and datasets from real
applications.


Date:     		Wednesday, 6 May 2009

Time:                   4:30pm-6:30pm

Venue:                  Room 3304
 			lifts 17-18

Committee Members:      Dr. Huamin Qu (Supervisor)
 			Dr. Chiew-Lan Tai (Chairperson)
                         Dr. Lei Chen
 			Prof. Long Quan


**** ALL are Welcome ****