METHODS FOR MINING CO-LOCATION PATTERNS WITH EXTENDED SPATIAL OBJECTS

被引：5
|
作者：
Bembenik, Robert [1 ]
Jozwicki, Wiktor [1 ]
Protaziuk, Grzegorz [1 ]
机构：
[1] Warsaw Univ Technol, Inst Comp Sci, Nowowiejska 15-19, PL-00665 Warsaw, Poland
来源：
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE | 2017年 / 27卷 / 04期
关键词：
spatial data mining; co-location patterns; extended objects; ASSOCIATION RULES; FRAMEWORK;
D O I：
10.1515/amcs-2017-0047
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 EXCOM are compared with DEOSP, highlighting the advantages and downsides of both approaches.
引用
收藏
页码：681 / 695
页数：15
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