- Student Participants
- Daniel Godfrey, University of North Carolina at Charlotte
- Caley Johns, Brigham Young University - Idaho
- Carol Sadek, Wofford College
- 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
state-of-the 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.
Poster presentation, 13th Annual North Carolina State University Undergraduate Summer Research
Symposium, Tally Center, NC State University, July 30, 2014.
Download The Poster (pdf).
Photos From The Poster Presentation
Cayley Johns, Daniel Godfrey, Carol Sadek.jpg
Chancellor Randy Woodson with the students.jpg
Danial Godfrey, Cayley Johns, Carol Sadek, Shaina Race.jpg
Carol Sadek Explaining to Chancellor Randy Woodson.jpg
Cayley Johns Explaining to Chancellor Randy Woodson.jpg
Shaina Race and Randy Woodson.jpg
Daniel Godfrey and Carol Sadek
Daniel, Carol, and Cayley with their poster.jpg