关键词: 邻近类别集, 非空真子集, 交叉搜索, 空间数据挖掘

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