顾及距离与形状相似性的面状地理实体聚类

基金项目: 国家863计划资助项目(2001AA135080)

详细信息

Clustering Analysis of Geographical Area Entities Considering Distance and Shape Similarity

¹Xi'an Research Institute of Surveying and Mapping,1 Middle Yanta Road,Xi'an 710054,China

Funds: 国家863计划资助项目(2001AA135080)

摘要: 与点状地理实体不同,面状地理实体不仅具有位置特征,还具有形状特征。对于面状地理实体而言,仅考虑距离因素设计聚类准则是不全面的。综合考虑距离和几何形状相似性来设计聚类准则,实现了相应的聚类算法。实验证明,该算法适合面状地理实体的聚类分析。
- 空间聚类 /
- 面状地理实体 /
- 相似性准则
Abstract: Geographical area entities are different from geographical point entities,because they have both position feature and shape feature.It is not enough for geographical area entities to be clustered if the clustering criterion just considers distance factor.The clustering criterion designed by us includes distance factor and geometry shape similarity factor.On the basis of this,the corresponding clustering algorithm was implemented.The experimental results show that the algorithm fits to clustering analysis of geographical area entities.
- spatial clustering /
- geographical area entities /
- similarity criterion