Working Group on Algorithms for Multidimensional Scaling I

Working Group Meeting: August 6 - 9, 2001

Public Workshop: Friday, August 10, 2001

Location: DIMACS Center, CoRE Building, Rutgers University

J. Douglas Carroll, Rutgers University,
Phipps Arabie, Rutgers University,
Lawrence J. Hubert, University of Illinois,

This material is based upon work supported by the National Science Foundation under Grant No. 0100921


Call for papers for Working Group meetings, Mon.-Wed., Aug. 6-8. Each participant in the DIMACS Working Group may submit a title and abstract for informal talk or talks that s/he would like to present. Talks should primarily focus on algorithmic aspects of MDS. Primary emphasis should be given to use of algorithms not typically used for fitting MDS models, such as linear and mixed integer programming, nonlinear or dynamic programming, and other optimization methods that have not been traditionally applied to fitting MDS and related models-- especially when these can be used to fit models not easily amenable to more traditional optimization techniques, such as various gradient based procedures. Another class of algorithmic issues to be considered has to do with approaches for increasing the size of data sets MDS and related methods can deal with, either via improvements in existing algorithms aimed at speeding them up considerably, as well as enabling them to deal with larger data sets, or by use of heuristic methods that may not precisely optimize a well defined criterion of fit. but may allow dealing with much larger data sets efficiently. Papers of this type can generically be classified as papers on MDS and related techniques for Massive Data Sets (MDS for MDS), or "Data Mining" in the context of MDS and related methodology.

The class of MDS and related models that can be dealt with include, in addition to spatial models for proximity data, multidimensional or multiattribute models for preferential choice or other multivariate data, non-spatial or discrete models, such as tree structure, (overlapping or non-overlapping) clustering models, or "hybrid" models combining aspects of continuous spatial and discrete non-spatial models (e.g., a model for proximity data in which proximities are related to a sum of distances from an MDS-like spatial model and an ultrametric or path length metric defined on one or more tree structures; alternatively, the discrete component could consist of distances or distance-like measures defined on pairs of objects based on, say, an overlapping clustering structure).

When submitting a title and abstract for such a talk that might be presented at the Working Group meetings, please indicate author or authors, if more than one, time needed to present material involved, and any other special needs (for audio-visual equipment, computer or other facilities that will be required as part of presentation, etc.). Submit information to Dr. Carroll via email:

A program committee will be appointed, jointly chaired by Doug Carroll and Phipps Arabie, as coorganizers of the Working Group, to formulate a detailed program for the three days of Working Group meetings and the Public Session on Friday.

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Document last modified on June 1, 2001.