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Chapter IX Enhancing the Process of Knowledge Discovery in Geographic Databases Using Geo-Ontologies
"... This chapter introduces the problem of mining frequent geographic patterns and spatial association rules from geographic databases. In the geographic domain most discovered patterns are trivial, non-novel, and noninteresting, which simply represent natural geographic associations intrinsic to geogra ..."
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This chapter introduces the problem of mining frequent geographic patterns and spatial association rules from geographic databases. In the geographic domain most discovered patterns are trivial, non-novel, and noninteresting, which simply represent natural geographic associations intrinsic to geographic data. A large amount of natural geographic associations are explicitly represented in geographic database schemas and geo-ontologies, which have not been used so far in frequent geographic pattern mining. Therefore, this chapter presents a novel approach to extract patterns from geographic databases using geo-ontologies as prior knowledge. The main goal of this chapter is to show how the large amount of knowledge represented in geo-ontologies can be used to avoid the extraction of patterns that are previously known as noninteresting. Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Enhancing the Process of Knowledge Discovery Geographic Databases Using Geo-Ontologes
Filtering Frequent Spatial Patterns with Qualitative Spatial Reasoning
"... In frequent geographic pattern mining a large amount of patterns can be non-novel and non-interesting. This problem has been addressed recently, and background knowledge is used to reduce well known geographic patterns. However, a large amount of meaningless patterns which is independent of domain k ..."
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In frequent geographic pattern mining a large amount of patterns can be non-novel and non-interesting. This problem has been addressed recently, and background knowledge is used to reduce well known geographic patterns. However, a large amount of meaningless patterns which is independent of domain knowledge is still extracted from geographic data. Therefore, this paper proposes a method for filtering specific types of meaningless spatial patterns using qualitative spatial reasoning. We proof a significant reduction of the number of frequent patterns, which is also shown with experiments performed on real data. These experiments even show a reduction in computational time. 1.
A Framework for Regional Association Rule Mining and Scoping in Spatial Datasets
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Learning and Transferring Geographically Weighted Regression Trees across Time
"... Abstract. The Geographically Weighted Regression (GWR) is a method of spatial statistical analysis which allows the exploration of geographical differences in the linear effect of one or more predictor vari-ables upon a response variable. The parameters of this linear regression model are locally de ..."
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Abstract. The Geographically Weighted Regression (GWR) is a method of spatial statistical analysis which allows the exploration of geographical differences in the linear effect of one or more predictor vari-ables upon a response variable. The parameters of this linear regression model are locally determined for every point of the space by processing a sample of distance decay weighted neighboring observations. While this use of locally linear regression has proved appealing in the area of spa-tial econometrics, it also presents some limitations. First, the form of the GWR regression surface is globally defined over the whole sample space, although the parameters of the surface are locally estimated for every space point. Second, the GWR estimation is founded on the assump-tion that all predictor variables are equally relevant in the regression surface, without dealing with spatially localized collinearity problems. Third, time dependence among observations taken at consecutive time points is not considered as information-bearing for future predictions. In this paper, a tree-structured approach is adapted to recover the func-tional form of a GWR model only at the local level. A stepwise approach is employed to determine the local form of each GWR model by selecting only the most promising predictors. Parameters of these predictors are estimated at every point of the local area. Finally, a time-space transfer technique is tailored to capitalize on the time dimension of GWR trees learned in the past and to adapt them towards the present. Experiments confirm that the tree-based construction of GWR models improves both the local estimation of parameters of GWR and the global estimation of parameters performed by classical model trees. Furthermore, the effec-tiveness of the time-space transfer technique is investigated. 1
