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

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.

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 The other writings to be withdrawn are 1503.03488 and 1501.07227

References in corpus (4)

• The Random-Cluster Model
• Continuum percolation of wireless ad hoc communication networks
• Monotone properties of random geometric graphs have sharp thresholds
• Continuum Limits of Markov Chains with Application to Network Modeling