An Algorithm of Constraint Frequent Neighboring Class Set Mining Based on Downward Search

Abstract:

As present constraint frequent neighboring class set mining algorithms have some redundancy candidate constraint frequent neighboring class set and some repeated computing. And so this paper proposes an algorithm of constraint frequent neighboring class set mining based on downward search, which is suitable for mining long constraint frequent neighboring class set from large spatial database via downward search. The algorithm adopts binary arrangement to turn spatial instance into integer, which is regarded as a spatial transaction, and it uses candidate domain mapping to create constraint frequent neighboring class set via downward search, namely, the algorithm creates candidate domain and uses an integer in the domain to map a candidate, downward search is that this integer of mapping candidate is from maximum to minimum. The method is different from traditional down search or top-down search. The experimental result indicates that the algorithm is more efficient than present constraint frequent neighboring class set mining algorithm when mining long constraint frequent neighboring class set.

• Periodical: Applied Mechanics and Materials (Volumes 66-68)
• Pages: 494-497
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.66-68.494
• Online since: July 2011
• Authors: Cheng Sheng Tu
• Keywords: Candidate Domain Mapping, Constraint Class, Downward Search, Frequent Neighboring Class Set, Spatial Data Mining

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