Relevance Criteria for Data Mining Using Error-Tolerant Graph Matching

摘要

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.