Date: 2018-04-20

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Standard clustering algorithms can find regular-structured clusters such as ellipsoidally- or spherically-dispersed groups, but are more challenged with groups lacking formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial algorithm that can be used with the computationally efficient k-means or other algorithms. Our approach computes the cumulative distribution function of the normed residuals from an appropriately fit k-groups model and calculates the nonparametric overlap between all pairs of groups. Groups with high pairwise overlap are merged as long as the generalized overlap decreases. Our methodology is always a top performer in identifying groups with regular and irregular structures in many datasets. We use our method to identify the distinct kinds of activation in a functional Magnetic Resonance Imaging study.

(This work is joint with Israel Almodovar-Rivera of the University of Puerto Rico and was supported in part by the US National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under its Award No. R21EB016212, The content of this paper however is solely the responsibility of the authors and does not represent the official views of either the NIBIB or the NIH.)

Speaker

Ranjan Maitra is a Professor in the Department of Statistics and Statistical Laboratory at the Iowa State University. His research includes: Analysis of Massive Datasets, Clustering, Data Mining, Finite Mixture Models, Image Analysis, functional Magnetic Resonance Imaging, Tomography, Statistical Computing, Simulation Algorithms.