Frequent neighboring class set mining
Gang Fang, Jiang Xiong, DU Xiang-lin, Xiaobin Tang · 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010
Aiming to these presented frequent neighboring class set mining algorithms have some repeated computing and redundancy candidate frequent neighboring class set when these algorithms extract frequent neighboring class set, this paper proposes an algorithm of mining frequent neighboring class set based on increasing sequence, which is suitable for mining frequent neighboring class set of objects in large spatial data. The algorithm uses the classification method to create database of neighboring class set, and then generates candidate frequent neighboring class set through the numerical ascending method, namely, it uses increasing sequence to generate candidate frequent neighboring class set, it only need scan once database to extract frequent neighboring class set. The algorithm improves mining efficiency through the numerical ascending method, since not only using the numerical ascending method to generate candidate is simple, but also using logic operation to compute support is very fast. The result of experiment indicates that the algorithm is faster and more efficient than presented algorithms when mining frequent neighboring class set in large spatial data.