Abstract #301903

This is the preliminary program for the 2003 Joint Statistical Meetings in San Francisco, California. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 2-5, 2003); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

Back to main JSM 2003 Program page
JSM 2003 Abstract #301903
Activity Number: 284
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Graphics
Abstract - #301903
Title: Multiresolution Visualization for Exploratory Data Analysis
Author(s): Matthew Ward*+
Companies: Worcester Polytechnic Institute
Address: 100 Institute Rd., Worcester, MA, 01609-2247,
Keywords: multivariate data visualization ; visual data mining ; hierarchical data visualization ; exploratory data analysis
Abstract:

A typical problem in visual data mining is that of scale; datasets to be analyzed often contain hundreds of thousands of records, each with potentially hundreds or thousands of dimensions or variables. These sizes easily exceed the capabilities of most, if not all, traditional data visualization techniques. This paper describes our efforts at managing the large-scale data analysis problem using multiresolution visualization methods in conjunction with interactive tools to facilitate the navigation and filtering of the data space. We use hierarchical clustering along three distinct directions, namely interrecord, interdimension, and intradimension (for nominal/categorical dimensions), thus enabling the creation of aggregations and summarizations at multiple levels of detail. We augment traditional multivariate visualization methods to convey cluster characteristics, and enable users to drill down on clusters of interest or consolidate uninteresting regions of the data. The resulting system enables users to visually explore datasets much larger than permitted by most visualization systems.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2003 program

JSM 2003 For information, contact meetings@amstat.org or phone (703) 684-1221. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2003