An Algorithm of Fast Mining Frequent Neighboring Class Set

Abstract

For these prombles that present frequent neighboring class set mining algorithms have more repeated computing and redundancy neighboring class sets, this paper proposes an algorithm of fast mining frequent neighboring class set, which is suitable for mining frequent neighboring class set of objects in large spatial data. The algorithm uses the approach of going back to create database of neighboring class set, and uses the approach of generating proper subset of neighboring class set to compute support by descending search, it only need scan once database to extract frequent neighboring class set. The algorithm improves mining efficiency by two ways. One is that it needn’t generate candidate frequent neighboring class set, the other is that it needn’t repeated scan database when computing support. The result of experiment indicates that the algorithm is faster and more efficient than present algorithms when mining frequent neighboring class set in large spatial data.