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Temporal data mining methodologies in a geo-spatial decision support system.

机译：地理空间决策支持系统中的时间数据挖掘方法。

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摘要

In this dissertation, temporal data mining methodologies are developed to facilitate knowledge discovery in the framework of a distributed Geo-spatial Decision Support System (GDSS), with a focus on drought risk management. In this process, climatic data are collected from a variety of sources at weather stations. However, there are two kinds of missing (or incomplete) data. First, data are partially missing because of temporary malfunction or unavailability of equipment. Imputation methods based on clustering and soft computing techniques are developed to solve this missing data problem. Second, some locations do not have local observed data due to cost, physical, or technical considerations. To generate association rules for these un-sampled locations, three spatial interpolation models are developed and integrated into the temporal data mining process.; After data preparation and preprocessing, we look more closely at the temporal property of time series data. Because a periodic pattern indicates something persistent and predictable, it is important to identify and characterize the periodicity. In this dissertation, an approach for mining partial periodic association rules in temporal databases is discussed. This approach allows the discovery of periodic episodes such that the events in an episode are not constrained by a fixed order. Moreover, this approach treats the antecedent and consequent of a rule separately and allows time lag between them. Thus, rules discovered are useful for prediction.; Additionally, droughts occur infrequently by nature. To facilitate drought risk management, it is important to discover infrequent episodes from multiple data sequences. In this dissertation, an algorithm is developed for the discovery of infrequent episodes with a combination of bottom-up and top-down scanning schema. The information sharing between bottom-up and top-down scanning helps prune candidate episodes, and thus, efficiently find infrequent episodes that are interesting to users.; Overall, the objective of this research is to enhance the body of work in the area of temporal data mining to enable knowledge discovery in the context of a GDSS.
机译：本文开发了时态数据挖掘方法，以在分布式地理空间决策支持系统（GDSS）的框架内促进知识发现，重点是干旱风险管理。在此过程中，从气象站的各种来源收集气候数据。但是，有两种丢失（或不完整）的数据。首先，由于暂时性故障或设备不可用而部分丢失数据。开发了基于聚类和软计算技术的插补方法来解决此丢失的数据问题。其次，由于成本，物理或技术方面的考虑，某些地区没有本地观测数据。为了生成这些未采样位置的关联规则，开发了三个空间插值模型并将其集成到时间数据挖掘过程中。在数据准备和预处理之后，我们将更仔细地研究时间序列数据的时间特性。由于周期性模式指示某些持久性和可预测性，因此识别并表征周期性非常重要。本文讨论了一种在时间数据库中挖掘部分周期性关联规则的方法。这种方法允许发现周期性情节，以使情节中的事件不受固定顺序的约束。而且，这种方法分别处理规则的前因和后因，并允许它们之间存在时间滞后。因此，发现的规则对于预测很有用。另外，自然干旱很少发生。为了促进干旱风险管理，重要的是从多个数据序列中发现偶发事件。本文研究了一种自下而上和自上而下的扫描模式相结合的发现罕见事件的算法。自下而上和自上而下的扫描之间的信息共享有助于修剪候选情节，从而有效地找到用户感兴趣的不频繁情节。总体而言，这项研究的目的是增强时态数据挖掘领域的工作体系，以实现在GDSS上下文中的知识发现。

著录项

  • 作者

    Li, Dan.;

  • 作者单位

    The University of Nebraska - Lincoln.;

  • 授予单位 The University of Nebraska - Lincoln.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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