<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

Published Online: Jan 13, 2018

Page range: 681 - 695

Received: Dec 06, 2016

Accepted: Aug 09, 2017

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

Keywords
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

Published Online: Jan 13, 2018

Page range: 681 - 695

Received: Dec 06, 2016

Accepted: Aug 09, 2017

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

Keywordsspatial data mining, co-location patterns, extended objects

© by Robert Bembenik

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

Keywords
spatial data mining, co-location patterns, extended objects