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Updated: May 29, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
A variation on a nonparametric clustering method
B Johnston1, T Bailey, R Dubes
1Department of Radiology, Michigan State University, East Lansing, MI 48824; Department of Computer Science, Michigan State University, East Lansing, MI 48824.
Abstract:
A single modification to a mode-seeking clustering algorithm proposed by Koontz, Narendra, and Fukunaga is shown to generate a novel clustering and to provide an indication of cluster stability. The modified method should provide better clusterings for ``uniform, touching'' clusters than the original, although the original should work better than the modified method for ``touching Gaussian'' clusters. Suitable ranges for the clustering parameters of both methods are investigated. Since the modification requires changing only one line of the original algorithm, two clusterings can be obtained for the price of one coding.
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