Abstract
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.
This research is partially supported by TUBITAK in the project with number 106E012.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Calargun, U.S.: Fuzzy Association Rule Mining From Spatio-Temporal Data: An Analysis of Meteorological Dat. In Turkey. In: Middle East Technical University (January 2008)
Isik, N.: Fuzzy Spatial Data Cube Construction And Its Us. In Association Rule Mining. In: Middle East Technical University (May 2005)
Stefanovic, N., Han, J., Koperski, K.: Grid Information Services for Distributed Resource SharingObject-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes. IEEE Transactions on Knowledge and Data Engineering 12(6) (2000)
Han, J., Kamber, M.: Data Mining: concepts and Techniques. Morgan Kaufmann Publisher, Inc, San Francisco (2001)
Turkish State Meteorological Service, http://www.meteor.gov.tr
ArcGIS: The Complete Enterprise GIS, http://www.esri.com/software/arcgis/
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, pp. 27–223 (1993)
Kuok, C.M., Fu, A., Wong, M.H.: Mining Fuzzy Association Rules in Databases. ACM SIGMOD Record 27, 41–46 (1998)
Xie, D.W.: Fuzzy Association Rules discovered on Effective Reduced Database Algorithm. In: IEEE Intl Conf on Fuzzy Systems, pp. 779–784 (2005)
Pestana, G., da Silva, M.M.: Multidimensional Modeling based on Spatial, Temporal and Spatio-Temporal Stereotypes. In: ESRI International User Conference (2005)
Mari, J.F., Le Ber, F.: Temporal and spatial data mining with second-order hidden markov models. In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 10(5), pp. 406–414. Springer, Heidelberg (2004)
Gonzalez, H., Han, J., Li, X., Klabjan, D.: Warehousing and Analysis of Massive RFID Data Sets. In: International Conference on Data Engineering (ICDE 2006) (April 2006)
Ning, H., Yuan, H., Chen, S.: Temporal Association Rules in Mining Method. In: First International Multi-Symposiums on Computer and Computational Sciences, vol. 2, pp. 739–742 (2006)
Delic, D., Lenz, H.J., Neiling, M.: Improving the Quality of Association Rule Mining by Means of Rough Sets. In: First International Workshop on Soft Methods in Probability and Statistics (September 2002)
Hipp, J., Guntzer, U., Nakhaeizadeh, G.: Algorithms for Association Rule Mining. ACM SIGKDD 2(1) (July 2001)
Zhang, C., Zhang, S.: Association Rule Mining: Models and Algorithms, pp. 25–46. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calargun, S.U., Yazici, A. (2008). Fuzzy Association Rule Mining from Spatio-temporal Data. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69839-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-69839-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69838-8
Online ISBN: 978-3-540-69839-5
eBook Packages: Computer ScienceComputer Science (R0)