On The Sharp Threshold Interval Length of Partially Connected Random Geometric Graphs During K-Means Classification

2014/12/13 by Robert A. Murphy, Murphy, Robert A. · 1 citation
Computer Science · Mathematics · #05C40 #05C80 #60G57 #60G60 #82B41 #Bayesian Methods and Mixture Models #Data Management and Algorithms #FOS: Mathematics #Probability (math.PR) #Statistical Methods and Inference #math.PR #msc:05C40 #msc:05C80 #msc:60G57 #msc:60G60 #msc:82B41

paper · pdf · doi:10.48550/arxiv.1412.4178

These writings are part of a longer writing which has been submitted for publication. I plan to replace this writing (and the other 2 writings) with the single writing that has been submitted for publication. The other writings to be withdrawn are 1503.03488 and 1501.07227

openalex publication_date 2014/12/13 · arxiv created 2016/02/10 · arxiv updated 2016/02/12 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

In K-means classification, a set of data will form clusters, i.e. classes, if the measured distances between data points (or some common point in each class) are below a certain threshold. With the assumption that the data points are randomly generated throughout some bounded region according to a certain probability distribution, we estimate the mean number of classes to form with high probability.

Citations

• Continuum percolation of wireless ad hoc communication networks

Cited by

• A Critical Connectivity Radius for Segmenting Randomly-Generated, High Dimensional Data Points

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