Workshop Theme and Goals:
Discrete mathematics (dm) and theoretical computer science (tcs) have become important tools in the areas of classification theory, consensus theory and pattern recognition (C2P). Looking at the types of problems attacked by the data mining community (as evidenced in the November 1996 issue of the Communications of the ACM), it is evident that cluster analysis (an area of classification theory) already plays a major role in massive data set (MDS) problems, though usually from a statistical point of view. It seems clear that dm/tcs-based C2P can play a role in this arena as well and that dm and tcs will eventually have important things to say in the analysis of very large, uncertain, heterogeneous data sets. This does not seem to be the case at present, so one of our main goals will be to explicate areas where C2P can play a central role (e.g., government, financial, ecological, astrophysical, molecular, etc.). This will be done in an exploratory workshop environment. There will be no specific applications that will be the focus of this workshop since we are looking for methodological approaches that involve, or might involve, the discrete mathematical aspects of C2P in MDS problems.
Participants:
Participants are expected to range from neophytes to experts in the field as well as practitioners who are users of C2P. All MDS talks will be expected to make some mention of C2P and/or discrete mathematics, broadly interpreted.
If you would like to give a talk, please contact one of the organizers. There will be a small amount of support available for participants who do not have other sources of support available. A list of speakers will be given as it develops.
Registration:
If you plan to attend, please send a registration form to Pat Pravato by email (pravato@dimacs.rutgers.edu).
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