Discovering fuzzy spatial association rules

Date

2002-04-04

Author

Kacar, E
Cicekli, NK

Metadata

Show full item record

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

Item Usage Stats

140
views

0
downloads

Discovering interesting, implicit knowledge and general relationships in geographic information databases is very important to understand and use these spatial data. One of the methods for discovering this implicit knowledge is mining spatial association rules. A spatial association rule is a rule indicating certain association relationships among a set of spatial and possibly non-spatial predicates. In the mining process, data is organized in a hierarchical manner. However, in real-world applications it may not be possible to construct a crisp structure for this data, instead some fuzzy structures should be used. Fuzziness, i.e. partial belonging of an item to more than one sub-item in the hierarchy, could be applied to the data itself, and also to the hierarchy of spatial relations. This paper shows that, strong association rules can be mined from large spatial databases using fuzzy concept and spatial relation hierarchies.

Subject Keywords

Data mining, Association rules, Spatial data, Geographic information databases, Fuzzy association rules

URI

https://hdl.handle.net/11511/65778

DOI

https://doi.org/10.1117/12.460216

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

An Embedded spatial statistics toolbox (R techniques) in open source GİS software (uDig) Çavur, Mahmut; Düzgün, H. Şebnem; Department of Geodetic and Geographical Information Technologies (2016) It is widely considered that geographic information systems (GIS) should include more spatial data analysis (SDA) techniques. The issues of which techniques should be included and how statistical analysis can be integrated with GIS are still widely debated. However, the typical software does not include all geospatial techniques. In this respect, this thesis focuses on the means to develop a framework which implements R spatial statistical techniques in the uDig GIS so that GIS and spatial statistical analy...
Fuzzy association rule mining from spatio-temporal data Calargun, Seda Unal; Yazıcı, Adnan (2008-07-03) The use of fuzzy sets in mining association rules from spatio-temporal databases is useful since fuzzy sets are able to model the uncertainty embedded in the meaning of data. There are several fuzzy association rule mining techniques that can work on spatio-temporal data. Their ability to mine fuzzy association rules has to be compared on a realistic scenario. Besides the performance criteria, other criteria that can express the quality of an association rule discovered shall be specified. In this paper, fu...
A GIS based spatial data analysis in Knidian amphora workshops in Reşadiye Kıroğlu, M. Fatih; Tuna, Numan; Düzgün, Şebnem; Department of Geodetic and Geographical Information Technologies (2003) The main objective of this study is to determine main activity locations and correlation between different artifact types in an archaeological site with geographical information systems (GIS) and spatial data analyses. Knidian amphora workshops in Datça peninsula are studied in order to apply GIS and spatial statistical techniques. GIS capabilities are coupled with some spatial statistical software and spatial data analysis steps are followed. Both point and area datasets are examined for the effective anal...
The strong partial transitive-closure problem: Algorithms and performance evaluation Toroslu, İsmail Hakkı (1996-08-01) The development of efficient algorithms to process the different forms of transitive-closure (To) queries within the context of large database systems has recently attracted a large volume of research efforts. In this paper, we present two new algorithms suitable for processing one of these forms, the so called strong partially instantiated transitive closure, in which one of the query's arguments is instantiated to a set of constants and the processing of which yields a set of tuples that draw their values...
Confidence-based concept discovery in relational databases Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2009-11-16) Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, we improve an ILP-based con...

Citation Formats

E. Kacar and N. Cicekli, “Discovering fuzzy spatial association rules,” 2002, vol. 4730, p. 94, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65778.