Visual Clustering in Parallel Coordinates and Graphs

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis 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 thesis, 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, a geometry-based 
edge grouping approach and an energy-based hierarchical visual clustering 
scheme are proposed. The effectiveness of these methods has been 
demonstrated through extensive experiments using both synthetic data and 
datasets from real applications.


Date:			Monday, 17 August 2009

Time:			4:00pm-6:00pm

Venue:			Room 3501
 			Lifts 25-26

Chairman:		Prof. Mitchell Tseng (IELM)

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Prof. Long Quan
 			Prof. Chiew-Lan Tai
 			Prof. Kai Tang (MECH)
 			Prof. Jian Huang (EECS, Univ. of Tennessee)


**** ALL are Welcome ****