Related Experiment Video
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
Extremes in the complexity of computing metric distances between partitions
1Department of Computer Science, Memorial University of Newfoundland, St. John's, Nfld., Canada A1C 5S7.
Abstract:
Day [3] describes an analytical model of minimum-length sequence (MLS) metrics measuring distances between partitions of a set. By selecting suitable values of model coordinates, a user may identify within the model that metric most appropriate to his classification application. Users should understand that within the model similar metrics may nevertheless exhibit extreme differences in their computational complexities. For example, the asymptotic time complexities of two MLS metrics are known to be linear in the number of objects being partitioned; yet we establish below that the computational problem for a closely related MLS metric is NP-complete.
Related Concept Videos
Extraction: Partition and Distribution Coefficients
For extracting a solute from an aqueous phase into an organic...
Distance Problem
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Absolute and Local Extreme Values
Wald-Wolfowitz Runs Test I
The test works...
Compacting Factor test
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
