从空间数据库中挖掘频繁邻近类别集的一种新算

基金项目: 国家自然科学基金资助项目(40301038)

详细信息

Mining Complete and Correct Frequent Neighboring Class Sets from Spatial Databases

1 Nanjing Institute of Geography and Limnology,CAS,73 East Beijing Road,Nanjing 210008,China;² Department of Computer Science and Engineering,Harbin Institute of Technology,92 Xidazhi Street,Harbin 150001,China

Funds: 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。
- 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
Abstract: 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.
- spatial database /
- spatial association rules /
- spatial co-location patterns /
- neighbor-ing class set /
- data mining