|Math Coffee: "What Do You Do When You Have Too Many Answers? Consensus Clustering and Non-negative Matrix Factorization" Chuck Wessell, NCSU
When: November 12,
Chambers BuildingTicket Required: No
Some clustering algorithms return a locally optimal solution for a particular (often random) starting point. To avoid accepting a bad clustering derived from a poor starting point, one can run the clustering algorithm a large number of times and then use the resulting solutions to arrive at a single, robust clustering. This process is known as consensus clustering. This talk will briefly cover the idea of clustering and then develop a process for consensus clustering the results from multiple runs of the non-negative matrix factorization algorithm. Results from using this methodology on North Carolina county-by-county election data will be shared.
We will gather in Mathematics Hall at 6:30 PM for our traditional light snacks.
Contact: Prof. Donna Molinek