Association rules discovery is a fundamental task in spatial data mining where data are naturally described at multiple levels of granularity. ARES is a spatial data mining system that takes advantage from this taxonomic knowledge on spatial data to mine multi-level spatial association rules. A large amount of rules is typically discovered even from small set of spatial data. In this paper we present a graph-based visualization that supports data miners in the analysis of multi-level spatial association rules discovered by ARES and takes advantage from hierarchies describing the same spatial object at multiple levels of granularity. An application on real-world spatial data is reported. Results show that the use of the proposed visualization technique is beneficial. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Appice, A., & Buono, P. (2005). Analyzing multi-level spatial association rules through a graph-based visualization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3533 LNAI, pp. 448–458). Springer Verlag. https://doi.org/10.1007/11504894_63
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