Relevance criteria for data mining using error-tolerant graph matching

Authors:
Sidharta Gautama;Rik Bellens;Guy De Tré;Johan D'Haeyer
Affiliations:
TELIN, Ghent University, Gent, Belgium;TELIN, Ghent University, Gent, Belgium;TELIN, Ghent University, Gent, Belgium;TELIN, Ghent University, Gent, Belgium
Venue:
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Year:
2006

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Abstract

In this paper we present a graph based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image information and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows to determine the bounds of error tolerance and in this way characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy the derived bounds of error tolerance are essential.