A cluster detection algorithm based on percolation theory

Craig Gotsman

doi:10.1016/0167-8655(91)90032-H

A cluster detection algorithm based on percolation theory

Craig Gotsman

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

We describe a novel algorithm for the detection of clusters of points embedded in background noise in the plane. The algorithm is based on the percolation phenomena found in random graphs obtained from a planar Poisson process. We estimate the time complexity of the algorithm and its expected performance.

Original language	English (US)
Pages (from-to)	199-202
Number of pages	4
Journal	Pattern Recognition Letters
Volume	12
Issue number	4
DOIs	https://doi.org/10.1016/0167-8655(91)90032-H
State	Published - Apr 1991
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence
Signal Processing
Computer Vision and Pattern Recognition

Keywords

Cluster
Poisson process
percolation
random graph

Access to Document

10.1016/0167-8655(91)90032-H

Cite this

@article{27df44b6a72f4c5b802c6b69fe2b5847,

title = "A cluster detection algorithm based on percolation theory",

abstract = "We describe a novel algorithm for the detection of clusters of points embedded in background noise in the plane. The algorithm is based on the percolation phenomena found in random graphs obtained from a planar Poisson process. We estimate the time complexity of the algorithm and its expected performance.",

keywords = "Cluster, Poisson process, percolation, random graph",

author = "Craig Gotsman",

year = "1991",

month = apr,

doi = "10.1016/0167-8655(91)90032-H",

language = "English (US)",

volume = "12",

pages = "199--202",

journal = "Pattern Recognition Letters",

issn = "0167-8655",

publisher = "Elsevier B.V.",

number = "4",

}

TY - JOUR

T1 - A cluster detection algorithm based on percolation theory

AU - Gotsman, Craig

PY - 1991/4

Y1 - 1991/4

N2 - We describe a novel algorithm for the detection of clusters of points embedded in background noise in the plane. The algorithm is based on the percolation phenomena found in random graphs obtained from a planar Poisson process. We estimate the time complexity of the algorithm and its expected performance.

AB - We describe a novel algorithm for the detection of clusters of points embedded in background noise in the plane. The algorithm is based on the percolation phenomena found in random graphs obtained from a planar Poisson process. We estimate the time complexity of the algorithm and its expected performance.

KW - Cluster

KW - Poisson process

KW - percolation

KW - random graph

UR - http://www.scopus.com/inward/record.url?scp=0025953123&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0025953123&partnerID=8YFLogxK

U2 - 10.1016/0167-8655(91)90032-H

DO - 10.1016/0167-8655(91)90032-H

M3 - Article

AN - SCOPUS:0025953123

SN - 0167-8655

VL - 12

SP - 199

EP - 202

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 4

ER -

A cluster detection algorithm based on percolation theory

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this