<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Methods for mining co–location patterns with extended spatial objects

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Methods for mining co\u2013location patterns with extended spatial objects"}

The paper discusses various approaches to mining co-location patterns with extended spatial objects. We focus on the properties of transaction-free approaches...

Methods for mining co–location patterns with extended spatial objects

Pubblicato online: 13 gen 2018

Pagine: 681 - 695

Ricevuto: 06 dic 2016

Accettato: 09 ago 2017

DOI: https://doi.org/10.1515/amcs-2017-0047

Parole chiave
spatial data mining, co-location patterns, extended objects

© by Robert Bembenik

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Methods for mining co–location patterns with extended spatial objects

Pubblicato online: 13 gen 2018

Pagine: 681 - 695

Ricevuto: 06 dic 2016

Accettato: 09 ago 2017

DOI: https://doi.org/10.1515/amcs-2017-0047

Parole chiavespatial data mining, co-location patterns, extended objects

© by Robert Bembenik

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
spatial data mining, co-location patterns, extended objects