
 Student Participants
 Daniel Godfrey, University of North Carolina at Charlotte
 Caley Johns, Brigham Young University  Idaho
 Carol Sadek, Wofford College
 Advisors
 Project Description
 Given unorganized data that may be derived from text
or simply raw numerics, the objective is to learn and develop
techniques for detecting, revealing, and analyzing hidden patterns and
clusters of information that exhibit some sort of similarity
or commonality. The size and diverse nature of the data sets of interest
make this a formidable but extremely important problem.

The first part of
the project will be to learn and understand how to use some of the
stateofthe art techniques by analyzing some selected practical
applications. Emphasis at the outset will be placed on text mining and community detection
although the content eventually can be directed by the
interests of the participants. Programming will be integral
as students implement existing methods and develop
their own improvements.

The ultimate goal is to explore possibilities for
developing some new methodologies and algorithms whose aim is
to detect patterns and structure in unlabeled data where no value
for error or accuracy can be placed on the final result.

The mathematics employed involves linear algebra, probability and
statistics, networks and graphs, and some numerical analysis coupled with
scientific computing principles.

Articles and Poster Presentations
