An Algorithm of Constraint Frequent Neighboring Class Sets Mining Based on Separating Support Items

For the reasons that present constraint frequent neighboring class sets mining algorithms need generate candidate frequent neighboring class sets and have a lot of repeated computing, and so this paper proposes an algorithm of constraint frequent neighboring class sets mining based on separating support items, which is suitable for mining frequent neighboring class sets with constraint class set in large spatial database. The algorithm uses the method of separating support items to gain support of neighboring class sets, and uses up search to extract frequent neighboring class sets with constraint class set. In the course of mining frequent neighboring class sets, the algorithm only need scan once database, and it need not generate candidate frequent neighboring class sets with constraint class set. By these methods the algorithm reduces more repeated computing to improve mining efficiency. The result of experiment indicates that the algorithm is faster and more efficient than present mining algorithms when extracting frequent neighboring class sets with constraint class set in large spatial database.