We consider the problem of spatial data mining where we find spatial association rules that contain spatial predicates. Various kinds of spatial predicates can be defined in the spatial association rules such as ``topological relationship,'' ``distance,'' ``orientation,'' and so forth. Among them, ``distance'' and ``orientation'' are quantitative predicates. In order to use such quantitative predicates in a rule, we have to specify values for them. For example, we set 10 miles distance as a short driving distance. Then, we can find rules that are derived from or that lead to the distance. However, implications of such specific values are differ depending on application domains. Therefore, in a data mining process, we need a fast algorithm for finding objective values for such quantitative predicates. In this paper, we present a data mining system, which uses efficient algorithms in computational geometry, for finding the optimized distance and / or orientation according to a specified criterion.
By: Yasuhiko Morimoto, Harunobu Kubo, Takeshi Kanda
Published in: RT0404 in 2002
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