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基于周期表的时空关联规则挖掘方法与实验

DOI: 10.3724/SP.J.1047.2011.00455, PP. 455-464

Keywords: 数据挖掘,关联规则,时空数据,层次挖掘,周期表

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Abstract:

地理现象的周期性往往掩盖了许多地学规律,这也是地学数据挖掘的一个主要内容。本文以周期表设计了一种时空层次关联规则挖掘方法——PRules-Miner。模型利用周期表的表现形式对时空数据进行组织,并通过两步挖掘过程发现具有"遥相关"地理事物间的变化模式。模型算法分为3个步骤(1)过滤周期表内无序数据逐行地提取多周期内时空状态的频繁项,生成新的时空频繁状态表;(2)基于向下闭合引理,对时空频繁状态表中的对象进行时空拓扑匹配,得到时空关联规则候选集;(3)对于候选数据集进行时空拓扑验证,得到时空关联规则集。为证明模型算法的可靠性,应用PO.DAAC提供的20年AVHRRProduct016海表面温度遥感反演数据集和国家气象科学院提供的南京地区降水逐日数据资料,研究大洋暖池与南京降水间的时空关联规则。实践表明,这种挖掘方法具有以下特点(1)算法基于面向对象思想,对地理对象状态进行独立描述。因此,所得时空关联规则与时空粒度无关,并能够挖掘出时空粒度不一致的地物间的关联关系。(2)算法使用笛卡尔积得到在时空拓扑阈值内匹配的时空候选集,并可以发现时域、空域均不邻接的事物间的时空关联规则,即时延不确定的地理现象的相互关联。

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