Spatial Data Mining (SDM) has great potential in supporting public policy and in underpinning society functioning. One task in SDM is the discovery of characterization and peculiarities of communities sharing socio-economic aspects in order to identify potentialities, needs and public intervention. Emerging patterns (EPs) are a special kind of pattern which contrast two classes. In this paper, we face the problem of extracting EPs from spatial data. At this aim, we resort to a multirelational approach in order to deal with the degree of complexity of discovering EPs from spatial data (i.e., (i) the spatial dimension implicitly defines spatial properties and relations, (ii) spatial phenomena are affected by autocorrelation). Experiments on real datasets are described.

Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach

CECI M;APPICE, ANNALISA;
2007-01-01

Abstract

Spatial Data Mining (SDM) has great potential in supporting public policy and in underpinning society functioning. One task in SDM is the discovery of characterization and peculiarities of communities sharing socio-economic aspects in order to identify potentialities, needs and public intervention. Emerging patterns (EPs) are a special kind of pattern which contrast two classes. In this paper, we face the problem of extracting EPs from spatial data. At this aim, we resort to a multirelational approach in order to deal with the degree of complexity of discovering EPs from spatial data (i.e., (i) the spatial dimension implicitly defines spatial properties and relations, (ii) spatial phenomena are affected by autocorrelation). Experiments on real datasets are described.
2007
978-3-540-74975-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/107265
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