摘要： 在空间数据库中挖掘带约束条件的频繁邻近类别集时，使用传统约束性关联规则的挖掘算法存在冗余候选项和重复计算等问题。为此，提出一种带约束条件的频繁邻近类别集挖掘算法，该算法以邻近类别集标识值双向变化的方法产生候选频繁邻近类别集，通过标识值的“与”运算计算支持数，达到提高算法挖掘效率的目的。实验结果表明，该算法比现有算法更简单快速。

关键词: 邻近类别集, 邻近约束类别集, 标识值, 双向搜索, 空间数据挖掘

Abstract: Redundancy candidate sets and repeated computation usually exist in traditional constrain rule mining algorithms with application to mining frequent neighboring class set with constrain condition. Hence, this paper proposes an algorithm of mining frequent Neighboring Class Set(NCS) with constrain condition, which is suitable for mining frequent neighboring class set with constrain spatial objects in spatial database. To improves mining efficiency of algorithm, the algorithm uses double alteration of identity value of neighboring class set to generate candidate frequent neighboring class set, and it also uses AND operation of identity value to compute support. Experimental result indicates that the algorithm is much simpler and faster than present algorithms.

Key words: Neighboring Class Set(NCS), neighboring constrain class set, identity value, double search, spatial data mining

中图分类号: 

• TP311