Fuzzy association rule mining from spatio-temporal data

Date

2008-07-03

Author

Calargun, Seda Unal
Yazıcı, Adnan

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

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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

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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.