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“多元”关联模式的时空数据挖掘 预览 被引量:4

Mining multivariate association patterns from spatiotemporal data
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摘要 为了从大量的时空数据集中挖掘类似于“星型”和“序列型”的“多元”关联规则模式,首先,针对要素同类和关系同质,提出“多元”关联模式的概念,即“多类别”和“多要素”下的“多规则”的关联组合;其次,用“图论”的方法构建常见的“星型”和“序列型”等“多元”关联模式;再次,提出“多元”关联模式的挖掘算法,引入“等价类”,搭建“多元”关联模式。合成实例(城市规划)说明“多元”关联模式的时空数据挖掘模式及其挖掘算法具有可用性。 To mine multivariate association patterns (MVAP), such as "star-like" and "sequence" multivariate association patterns, from large spatiotemporal datasets, a combination of multi-class-features (MCF) and multi-association-rules (MAR) was proposed according to entity types and relationships. The MVAP was then established based on the methods of graph theory. A new MVAP algorithm was presented by introducing "Equivalence Classes" to quickly identify MVAP. By use of an example in urban planning used to explain the MVAP process for spatiotemporal data mining, the results show that the MVAP and their algorithm have applicability.
作者 陈新保 LISong-nian 朱建军 陈建群 CHEN Xin-bao^ l, 2 LI Song-nian^2, ZHU Jian-jun^1, CHEN Jian-qun (1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China; 2. Department of Civil Engineering, Ryerson University, Toronto, M5B 2K3 Canada)
出处 《中南大学学报:自然科学版》 EI CAS CSCD 北大核心 2011年第1期 106-114,共9页 Journal of Central South University:Science and Technology
基金 国家留学基金委资助项目(留金[2007]3020)
关键词 “多元”关联模式 要素关联规则 等价类 nultivariate association patterns feature association rules equivalence classes
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参考文献28

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