Percolation in the k-nearest neighbor graph

نویسندگان

  • P. Balister
  • B. Bollobás
چکیده

Let P be a Poisson process of intensity one in R2. For a fixed integer k, join every point of P to its k nearest neighbors, creating a directed random geometric graph ~ Gk(R). We prove bounds on the values of k that, almost surely, result in an infinite connected component in ~ Gk(R) for various definitions of “component”. We also give high confidence results for the exact values of k needed. In particular, for percolation on the underlying (undirected) graph of ~ Gk(R), we prove that k = 11 is sufficient, and show with high confidence that k = 3 is the actual threshold for percolation.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Random nearest neighbor and influence graphs on Zd

Random nearest neighbor and influence graphs with vertex set Zd are defined and their percolation properties are studied. The nearest neighbor graph has (with probability 1) only finite connected components and a superexponentially decaying connectivity function. Influence graphs (which are related to energy minimization searches in disordered Ising models) have a percolation transition. © 1999...

متن کامل

FUZZY K-NEAREST NEIGHBOR METHOD TO CLASSIFY DATA IN A CLOSED AREA

Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.  

متن کامل

Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data

Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

متن کامل

Nearest neighbor and hard sphere models in continuum percolation

Consider a Poisson process X in Rd with density 1. We connect each point of X to its k nearest neighbors by undirected edges. The number k is the parameter in this model. We show that, for k = 1, no percolation occurs in any dimension, while, for k = 2, percolation occurs when the dimension is sufficiently large. We also show that if percolation occurs, then there is exactly one infinite cluste...

متن کامل

An Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008