Fuzzy association rule mining from spatio-temporal data

Date

2008-07-03

Author

Calargun, Seda Unal
Yazıcı, Adnan

Metadata

Show full item record

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

Item Usage Stats

215
views

0
downloads

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, fuzzy association rule mining is performed with spatio-temporal data cubes and Apriori algorithm. A real life application is developed to compare data cubes and Apriori algorithm according to the following criteria: interpretability, precision, utility, novelty, direct-to-the-point, performance and visualization, which are defined within the scope of this paper.

Subject Keywords

Data mining, Fuzzy association rules, Fuzzy spatio-temporal data cube, Association rule mining, Association rule mining comparison criteria

URI

https://hdl.handle.net/11511/46769

DOI

https://doi.org/10.1007/978-3-540-69839-5_47

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Confidence-based concept discovery in multi-relational data mining Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2008-03-21) 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, a new ILP-based concept dis...
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...
Improving the scalability of ILP-based multi-relational concept discovery system through parallelization Mutlu, Ayşe Ceyda; Karagöz, Pınar; Kavurucu, Yusuf (2012-03-01) Due to the increase in the amount of relational data that is being collected and the limitations of propositional problem definition in relational domains, multi-relational data mining has arisen to be able to extract patterns from relational data. In order to cope with intractably large search space and still to be able to generate high-quality patterns. ILP-based multi-relational data mining and concept discovery systems employ several search strategies and pattern limitations. Another direction to cope w...
Aggregation in confidence-based concept discovery for multi-relational data mining Kavurucu, Yusuf; Senkul, Pinar; Toroslu, İsmail Hakkı (null; 2008-12-01) 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 describe a method for ge...
Normalization and lossless join decomposition of similarity-based fuzzy relational databases Bahar, O; Yazıcı, Adnan (Wiley, 2004-10-01) Fuzzy relational database models generalize the classical relational database model by allowing uncertain and imprecise information to be represented and manipulated. In this article, we introduce fuzzy extensions of the normal forms for the similarity-based fuzzy relational database model. Within this framework of fuzzy data representation, similarity, conformance of tuples, the concept of fuzzy functional dependencies, and partial fuzzy functional dependencies are utilized to define the fuzzy key notion, ...

Citation Formats

S. U. Calargun and A. Yazıcı, “Fuzzy association rule mining from spatio-temporal data,” 2008, vol. 5072, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46769.