Analyzing Multi-level Spatial Association Rules Through a Graph-Based Visualization

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.

• Type: Conference
• Year: 2005
• Where: IEAAIE
• Authors: Annalisa Appice, Paolo Buono
• Keywords: IEAAIE 2005, Spatial Association Rules, Spatial Data, Spatial Data Mining