We propose a general mechanism to represent the spatial transactions in a way that allows the use of the existing data mining methods. Our proposal allows the analyst to exploit the layered structure of geographical information systems in order to define the layers of interest and the relevant spatial relations among them. Given a reference object, it is possible to describe its neighborhood by considering the attribute of the object itself and the objects related by the chosen relations. The resulting spatial transactions may be either considered like “traditional” transactions, by considering only the qualitative spatial relations, or their spatial extension can be exploited during the data mining process. We explore both these cases. First we tackle the problem of classifying a spatial dataset, by taking into account the spatial component of the data to compute the statistical measure (i.e., the entropy) necessary to learn the model. Then, we consider the task of extracting spatial association rules, by focusing on the qualitative representation of the spatial relations. The feasibility of the process has been tested by implementing the proposed method on top of a GIS tool and by analyzing real world data.

Knowledge Discovery from spatial transactions

TURINI, FRANCO
2007

Abstract

We propose a general mechanism to represent the spatial transactions in a way that allows the use of the existing data mining methods. Our proposal allows the analyst to exploit the layered structure of geographical information systems in order to define the layers of interest and the relevant spatial relations among them. Given a reference object, it is possible to describe its neighborhood by considering the attribute of the object itself and the objects related by the chosen relations. The resulting spatial transactions may be either considered like “traditional” transactions, by considering only the qualitative spatial relations, or their spatial extension can be exploited during the data mining process. We explore both these cases. First we tackle the problem of classifying a spatial dataset, by taking into account the spatial component of the data to compute the statistical measure (i.e., the entropy) necessary to learn the model. Then, we consider the task of extracting spatial association rules, by focusing on the qualitative representation of the spatial relations. The feasibility of the process has been tested by implementing the proposed method on top of a GIS tool and by analyzing real world data.
Rinzivillo, S; Turini, Franco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/111643
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