Relational Data Mining
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Combining visual techniques for Association Rules exploration
Proceedings of the working conference on Advanced visual interfaces
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Rule-chain incremental mining algorithm based on directed graph
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
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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.