When: Thursday, November 29, 2012 @ 3:30PM
Where: Olin Hall Room 321
Presenter: Brianna Heggeseth, University of California, Berkeley
Through the integration of technology into practically every aspect of our daily lives, it is becoming increasingly possible to collect massive amounts of data on individuals over time. My work revolves around finding meaning and structure in these large longitudinal data sets. One way to discover structure is through calculated clustering or grouping of the data. Implicit in any grouping of objects is a definition of similarity/dissimilarity between the objects, which is highly dependent on the scientific research question. I will introduce two standard clustering approaches, K-means and Finite Mixture Models, used for longitudinal data and illustrate how these algorithms indirectly define similarity. Then I will propose a new clustering method that focuses on a different definition of similarity. Throughout the talk, these methods will be discussed with reference to childhood growth patterns of individuals in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) study.
Refreshments will be served.
This presentation is part of the MCS Seminar series; please see the calendar of upcoming events.