从空间数据库中挖掘频繁邻近类别集的一种新算法  

Mining Complete and Correct Frequent Neighboring Class Sets from Spatial Databases

作　　者：马荣华[1] 何增友[2] 

机构地区：[1]中国科学院南京地理与湖泊研究所,南京市北京东路73号210008 [2]哈尔滨工业大学计算机系,哈尔滨市西大直街92号150001

出　　处：《武汉大学学报（信息科学版）》2007年第2期112-114,共3页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(40301038)

摘　　要：提出了一个邻近类别集挖掘的新算法。与已有算法相比,新算法能够找到完备、正确的邻近类别集的集合,并且给出了算法正确性和完备性的理论证明。A recent work has introduced the problem of mining neighboring class sets, where instances of each class of a neighboring class set are grouped using their Euclidean distances from each other. Although the concept of neighboring class sets is a useful one, the effective computation of frequent neighboring class sets is only partially solved. A novel algorithm for mining frequent neighboring class sets from spatial datasets is presented. Compared to the previous algorithm, the algorithm can discover complete and correct frequent neighboring class sets.

关 键 词：空间数据库 空间关联规则 关联位置模式 邻近类别集 数据挖掘 

分 类 号：P208[天文地球—地图制图学与地理信息工程]