Ranjan Maitra
Department of Statistics, Iowa State University

Kernel Nonparametric Overlap-based Syncytial Clustering

Commonly-used clustering algorithms usually find regular-structured clusters such as groups with ellipsoidal or spherical dispersions, but are more challenged  when the underlying groups lack formal structure or definition. Syncytial clustering is the name that we give 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 clustering algorithm that can be used with k-means and 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 each pair of clusters. 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 several datasets and can be applied to datasets with incomplete records, scatter and other extended scenarios.

(This work is joint with Israel A. Almodovar-Rivera of the University of Puerto Rico, but touches  work done with Ivan Ramler of St. Lawrence University, Volodymyr Melnykov of the University of Alabama, Anna Peterson and Arka Ghosh of Iowa State University, Andrew Lithio of Lilly and Nicholas Berry of Berry Consultants. 

The research was funded in part by the 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.)

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