实例位置模糊的空间co-location模式挖掘研究  

Mining Spatial Co-location Patterns for Fuzzy Location of Instances

作　　者：欧阳志平[1] 王丽珍[1] 周丽华[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：《计算机科学与探索》2012年第12期1144-1152,共9页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金 Nos. 61063008; 61272126;云南省自然科学基金 No. 2010CD025~~

摘　　要：实例位置模糊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地理信息系统(geographic information system,GIS)。研究了实例位置模糊的空间co-location模式挖掘问题。定义了实例位置模糊的空间co-location模式挖掘的相关概念,包括实例位置模糊、位置参与率等;给出了基本算法来挖掘实例位置模糊的co-location模式;提出了两种改进算法,即基于网格的距离计算和减枝候选模式,以提高挖掘性能,加快co-location规则的产生。通过大量的实验,说明了基本算法及其改进算法的效果和效率。Fuzzy location of instances can be applied to many areas, such as biomedical image databases, geographic information system （GIS） and more. This paper investigates the spatial co-location patterns mining problem for fuzzy location of instances. Firstly, it defines the related concepts of co-location patterns mining for fuzzy location of instances, including fuzzy location of instances, location participation ratio, etc. Secondly, it proposes a basic algorithm to mine co-location patterns from fuzzy location of instances. Then, it puts forward two kinds of the improved algorithms, grid-based distance calculation and the pruning candidate patterns, so as to improve the mining performance and accelerate the co-location rule generation. Finally, by extensive experiments, this paper verifies the efficiency and effectiveness of the algorithms.

关 键 词：空间数据挖掘 co—location模式 实例位置模糊 位置参与率 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]