On The Sharp Threshold Interval Length of Partially Connected Random Geometric Graphs During K-Means Classification
Probability
2016-02-12 v4
Abstract
In -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.
Cite
@article{arxiv.1412.4178,
title = {On The Sharp Threshold Interval Length of Partially Connected Random Geometric Graphs During K-Means Classification},
author = {Robert A. Murphy},
journal= {arXiv preprint arXiv:1412.4178},
year = {2016}
}
Comments
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 The other writings to be withdrawn are 1503.03488 and 1501.07227