Frequent neighboring class set mining with constraint condition

As these present frequent neighboring class set mining algorithms aren´t able to extract frequent neighboring class set with constraint condition, this paper proposes an algorithm of mining frequent neighboring class set with constraint condition based on down-up search strategy, which is suitable for mining frequent neighboring class set with constraint spatial classes in large spatial data. The algorithm uses the digital method to create database of neighboring class set, and then generates candidate frequent neighboring class set with constraint condition through down-up search strategy, namely, it connects two k-frequent neighboring class sets with constraint classes to generate (k+1)-candidate frequent neighboring class set with constraint classes, it only need scan once database to extract frequent neighboring class set with constraint condition. The algorithm uses logic operation to compute support of candidate frequent neighboring class set with constraint condition, it is very fast. The result of experiment indicates that the algorithm is fast and more efficient when mining short frequent neighboring class set with constraint condition in large spatial data.