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Standard clustering algorithms usually 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 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 groups. Groups with high pairwise overlaps 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. The approach is also used to identify the distinct kinds of gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and also the distinct kinds of activation in a functional Magnetic Resonance Imaging study.


This is a pre-print of the article Almodóvar-Rivera, Israel, and Ranjan Maitra. "Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering." arXiv preprint arXiv:1805.09505 (2018). Posted with permission.

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