Methods for mining co–location patterns with extended spatial objects

Robert Bembenik; Wiktor Jóźwicki; Grzegorz Protaziuk

doi:10.1515/amcs-2017-0047

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Methods for mining co–location patterns with extended spatial objects

International Journal of Applied Mathematics and Computer Science

Volume 27 (2017): Issue 4 (December 2017)

By: Robert Bembenik, Wiktor Jóźwicki and Grzegorz Protaziuk

Open Access

|Jan 2018

Abstract

The paper discusses various approaches to mining co-location patterns with extended spatial objects. We focus on the properties of transaction-free approaches EXCOM and DEOSP, and discuss the differences between the method using a buffer and that employing clustering and triangulation. These theoretical differences between the two methods are verified experimentally. In the performed tests three different implementations of EXCOMare compared with DEOSP, highlighting the advantages and downsides of both approaches.

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Articles in this issue

DOI: https://doi.org/10.1515/amcs-2017-0047 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 681 - 695

Submitted on: Dec 6, 2016

Accepted on: Aug 9, 2017

Published on: Jan 13, 2018

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

spatial data mining,

co-location patterns,

extended objects

Related subjects:

Mathematics,

Applied mathematics

© 2018 Robert Bembenik, Wiktor Jóźwicki, Grzegorz Protaziuk, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 27 (2017): Issue 4 (December 2017)