Nicoleta Serban
Assistant Professor, Industrial Systems and Engineering School, Georgia Institute of Technology

"Model-Based Data Mining for Functional Data Under Spatial Interdependence" 

Abstract:

In this seminar, I will present data mining methods for discovering and summarizing patterns in functional data observed under spatial interdependence. The field of functional data analysis has already provided a series of competitive data mining approaches, but they are generally limited to the assumption of independence between the random functions. This assumption is rather restrictive in many research applications.

In the first part of this seminar, I will introduce a model-based method for clustering random functions which are spatially interdependent. The time functions are decomposed into spatial global and time-dependent cluster effects using a semi-parametric model. We assume that the clustering membership is a realization from a Markov random field. In the case study presented in this paper, we focus on obtaining temporal cluster trends for racial-ethnic diversity for five southeast states in the US.

In the second part of this seminar, I will introduce a computational efficient and theoretically-founded cross-correlation analysis. Under the proposed semi-parametric model, we show that the cross-correlation estimators are asymptotically unbiased under the conditions that the sample size is large and the intrinsic dimensionality of the functional processes is much smaller than the sample size. We illustrate this correlation analysis within a demographic study, in which we analyze the association between per capita income and racial-ethnic diversity.

This is joint work with Huijing Jiang, PhD student in ISyE, Georgia Institute of Technology

Bio:

Nicoleta Serban is an assistant professor in ISyE. She received her B.S. in Mathematics and an M.S. in Theoretical Statistics and Stochastic Processes from the University of Bucharest. She went on to earn her Ph.D. in Statistics at Carnegie Mellon University. Before joining Georgia Tech, Dr. Serban's research focused on nonparametric statistical methods motivated by recent applications from proteomics and genomics. Dr Serban's current research focusses on multiple functional estimation and clustering with applications to industrial performance, service site location, socio-economics and NMR biomolecular studies. Visit Dr. Serban's website.

Please join us for a reception preceding the seminar outside Klaus 1324, beginning at 1:30pm.

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