模糊对象的空间Co-location模式挖掘研究  

Mining Spatial Co-Location Patterns for Fuzzy Objects

作　　者：欧阳志平[1] 王丽珍[1] 陈红梅[1] 

机构地区：[1]云南大学信息学院计算机科学与工程系,昆明650091

出　　处：《计算机学报》2011年第10期1947-1955,共9页Chinese Journal of Computers

基　　金：国家自然科学基金项目(61063008);云南省应用基础研究基金项目(2010CD025);云南大学科学研究基金项目(2009F29Q)资助~~

摘　　要：空间co-location模式表示的是空间对象的实例在一个相同的区域内频繁地空间并置.过去人们已经对确定及不确定数据的co-location模式挖掘问题进行了一些研究,但是针对模糊对象上进行的研究还没有.模糊对象在许多领域里都有着非常重要的应用,比如生物医学图像数据库和GIS.该文研究模糊对象的空间co-location模式挖掘问题.首先,定义模糊对象上空间co-location模式挖掘的相关概念,包括模糊参与率、模糊参与度等.其次,提出FB算法挖掘模糊对象的co-location模式.接着,提出了3种改进算法,包括剪枝对象、减少实例间连接、改进剪枝步,以提高挖掘性能、加快co-location规则的产生.最后通过大量的实验说明FB算法及其改进算法的效果和效率.A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region.The mining co-location pattern problem for certain and uncertain data had been investigated in the past,but not for fuzzy objects.Fuzzy objects could be applied to many areas such as biomedical image databases,GIS and more.This paper investigates the spatial co-location pattern mining problem for fuzzy objects.Firstly,it defines the related concepts of spatial co-location patterns mining on fuzzy objects,including fuzzy participation ratio,fuzzy participation index,etc.Secondly,this paper proposes an FB algorithm to mine co-location patterns from fuzzy objects.Then,three kinds of the improved algorithms,the pruning objects,reducing of the operation joining between spatial instances and optimizing the pruning steps,are put forward so as to improve the mining performance and accelerate the co-location rule generation.Finally,by extensive experiments,the efficiency and effectiveness of the algorithms are verified.

关 键 词：模糊对象 CO-LOCATION模式 空间数据挖掘 模糊参与率 减少连接 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]