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Reseach Article

An Image Processing based Algorithm for Discovering Co-Location Patterns

by Shahbaz Ahmad, Muhammad Asif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 1
Year of Publication: 2016
Authors: Shahbaz Ahmad, Muhammad Asif

Shahbaz Ahmad, Muhammad Asif . An Image Processing based Algorithm for Discovering Co-Location Patterns. International Journal of Computer Applications. 156, 1 ( Dec 2016), 1-6. DOI=10.5120/ijca2016912338

@article{ 10.5120/ijca2016912338,
author = { Shahbaz Ahmad, Muhammad Asif },
title = { An Image Processing based Algorithm for Discovering Co-Location Patterns },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 1 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912338 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:01:23.110745+05:30
%A Shahbaz Ahmad
%A Muhammad Asif
%T An Image Processing based Algorithm for Discovering Co-Location Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 1
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Spatial co-location patterns represents the subset of Boolean spatial features (e.g. Frontage roads, freeways) whose instances are often located in close geographic proximity. For instance, stagnant water founts and west Nile ailments are often co-located. The co-location pattern can be defined as an undirected connected graph in which every node represents a feature and every single edge denotes relationship (neighbourhood) between connecting features. Literature provides different approaches (including transaction based, join and join-less approaches) to discover co-location patterns. This paper proposes, implements and tests an image processing based algorithm to discover these patterns. The algorithm inputs minimum confidence measure (for statistical significance), neighbourhood distance threshold and set of Boolean spatial features, whose instances are represented as an image. It converts the image into binary image and then uses the concept of neighbourhood relationship (materialized using distance threshold) and confidence measure to mine the patterns. Furthermore, this paper provides implementation and testing of proposed algorithm in terms of time and space complexity.

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Index Terms

Computer Science
Information Sciences


Association rule mining Co-Location Pattern discovery Collocation Pattern Image Processing Spatial Association Pattern Spatial Data mining