基于最小拟合误差平方和准则的空间聚类  

Clustering Spatial Data Based on Minimum Square Fitting Error Sum

作　　者：滕明贵[1] 王儒敬[1] 马献章[2] 

机构地区：[1]中国科学技术大学自动化系,安徽合肥230026 [2]成都军区通信部,安徽合肥610011

出　　处：《小型微型计算机系统》2005年第7期1221-1224,共4页Journal of Chinese Computer Systems

基　　金：国家自然科学基金重点项目(69835010)资助

摘　　要：在一些回归分析问题中,数据来自于空间对象的非空间属性.许多问题中不用考虑空间属性,而直接分析非空间属性.如果在整个问题空间中,对象空间差异较大,需要将空间对象划分为若干子空间,子空间对应的局域回归模型,可以减小空间差异性的影响.针对子空间连通性约束情况下空间对象的局域回归分析问题,提出基于最小拟合误差平方和准则的空间划分方法,从一个空间的初始划分开始,按照拟合误差平方和下降的原则调整子空间边界,获得新的空间划分和对应的回归模型,不断迭代直到准则函数收敛.In many regression analyses, data are connected with spatial objects. Most cases ignore the spatial attributes and deal only with non-spatial attributes. When diversity between spatial objects play important role, local regressions are adopted. The space is divided into several sub-spaces, each corresponding to a local regression, which decrease the effect of spatial diversity. To solve local regression with connectivity constraint of spatial objects in sub-spaces, this paper proposes an algorithm for clustering spatial objects based on the criteria of the minimum square error sum. After initializing original partitioning, the boundaries of sub-spaces are adjusted to construct new partitioning. Such process will continue until the criteria function converges.

关 键 词：空间聚类 拟合误差 回归分析 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]