Abstract: Aiming at shortcoming that present frequent neighboring class set mining algorithms have superfluous computing because of generating candidate，this paper proposes an algorithm of frequent neighboring class set mining without candidate，which is suitable for mining frequent neighboring class set of spatial objects in large data.The algorithm uses the way of generating nonvoid proper subset of neighboring class set in crossing search to compute support.It only need once scan database to mine frequent neighboring class set.The algorithm improves mining efficiency by these approaches.One is that it needn’t generate candidate frequent neighboring class set，the other is that it needn’t repeat scanning 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 sets in large spatial data.

Key words: neighboring class set, nonvoid proper subset, crossing search, spatial data mining

摘要： 针对现有的频繁邻近类别集挖掘算法因产生候选项而存在冗余计算，提出一种无候选项的频繁邻近类别集挖掘算法，其适合在海量数据中挖掘空间对象的频繁邻近类别集；该算法以交叉搜索方式，用产生邻近类别集非空真子集的方法来计算支持数，实现一次扫描数据库挖掘频繁邻近类别集。算法无需产生候选频繁邻近类别集，且计算支持数时无需重复扫描数据库，达到了提高挖掘效率的目的。实验结果表明其在海量空间数据中挖掘频繁邻近类别集时，该算法比现有算法更快速更有效。

关键词: 邻近类别集, 非空真子集, 交叉搜索, 空间数据挖掘