Abstract
An algorithm of constrained association rules mining was presented in order to search for some items expected by people. Since some presented algorithms of association rules mining based on binary are complicated to generate frequent candidate itemsets, they may pay out heavy cost when these algorithms are used to extract constrained spatial association rules. And so this paper proposes an algorithm of constrained spatial association rules based on binary, the algorithm is suitable for mining constrained association among some different spatial objects under the same spatial pattern, which uses the way of ascending value to generates frequent candidate itemsets and digital character to reduce the number of scanned transaction in order to improve the efficiency. The experiment indicates that the algorithm is faster and more efficient than theses presented algorithms based on binary when mining constrained spatial association rules from spatial database.
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Fang, G., Wei, Z., Yin, Q. (2008). An Algorithm of Constrained Spatial Association Rules Based on Binary. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_3
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DOI: https://doi.org/10.1007/978-3-540-87734-9_3
Publisher Name: Springer, Berlin, Heidelberg
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