Publication
Title
Cohesion based co-location pattern mining
Author
Abstract
Because of a wide range of applications, e.g., GPS applications and location based services, spatial pattern discovery is an important task in data mining. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial structure by measuring the cohesion of a pattern. We present two ways to build the co-location pattern miner, FromOne and FromAll, in an attempt to find a balance between accuracy and runtime. Additionally, we propose a method named Fre-ball to transform a structure into a transaction database, after which any existing itemset mining algorithm can be used to find the co-location patterns. An experimental evaluation shows that FromOne and Fre-ball are more efficient than existing methods. The usefulness of our methods is demonstrated by applying them on the publicly available geographical data of the city of Antwerp in Belgium.
Language
English
Source (book)
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
Publication
S.l. : IEEE, 2015
Volume/pages
p. 1-10
ISI
000380468400059
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 12.04.2016
Last edited 21.07.2017
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