An Efficient Fitness Assignment Based on Dominating Tree

Abstract

A special challenge for spatial data mining is that information is not distributed uniformly in spatial data sets. Consequently, the discovery of regional knowledge is of fundamental importance. Unfortunately, regional patterns frequently fail to be discovered due to insuf- ficient global confidence and/or support in traditional association rule mining. Regional association rules, by definition, only hold in a subspace but not in the global space. One novel challenge is how to evaluate the impact of regional association rules. This paper centers on regional association rule scoping. We intro- duce a reward-based region discovery framework that employs clustering to find places where regional asso- ciation rules are valid. We evaluate our approach in a real-world case study to discover arsenic risk zones in the Texas water supply. The experimental results are validated by domain experts and compared with pub- lished results on arsenic contamination.