Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Structural geography of the space of emerging patterns
Intelligent Data Analysis
Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Discovering controlling factors of geospatial variables
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Discovery of feature-based hot spots using supervised clustering
Computers & Geosciences
Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets
IEEE Transactions on Information Technology in Biomedicine
Exploring labeled spatial datasets using association analysis
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
ESTATE: strategy for exploring labeled spatial datasets using association analysis
DS'10 Proceedings of the 13th international conference on Discovery science
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Modeling spatially distributed phenomena in terms of its controlling factors is a recurring problem in geoscience. Most efforts concentrate on predicting the value of response variable in terms of controlling variables either through a physical model or a regression model. However, many geospatial systems comprises complex, nonlinear, and spatially non-uniform relationships, making it difficult to even formulate a viable model. This paper focuses on spatial partitioning of controlling variables that are attributed to a particular range of a response variable. Thus, the presented method surveys spatially distributed relationships between predictors and response. The method is based on association analysis technique of identifying emerging patterns, which are extended in order to be applied more effectively to geospatial data sets. The outcome of the method is a list of spatial footprints, each characterized by a unique "controlling pattern"--a list of specific values of predictors that locally correlate with a specified value of response variable. Mapping the controlling footprints reveals geographic regionalization of relationship between predictors and response. The data mining underpinnings of the method are given and its application to a real world problem is demonstrated using an expository example focusing on determining variety of environmental associations of high vegetation density across the continental United States.