An Algorithm of Constraint Frequent Neighboring Class Set Mining Based on Interval Mapping

Abstract

Since the algorithms of constraint frequent neighboring class set mining based on Apriori has some redundancy candidate constraint frequent neighboring class set and some repeated computing, so its efficiency isn’t improved. Hence, this paper proposes an algorithm of constraint frequent neighboring class set mining based on interval mapping, which may efficiently extract short constraint frequent neighboring class set from large spatial database via up search. The algorithm uses binary weights to change neighboring class set into integer, which is looked on as a spatial transaction, and it uses interval mapping to generate constraint frequent neighboring class set via up search, i.e. the algorithm creates an interval to map a range of generating candidate, up search is mapping candidate from minimum to maximum of the interval. The method is different from traditional up search or down-up search. The experimental result indicates that the algorithm is more efficient than the constraint frequent neighboring class set mining algorithm based on Apriori when mining short constraint frequent neighboring class set.