Exploratory spatio-temporal data mining and visualization
Exploratory spatio-temporal data mining and visualization
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DOI：
10.1016/j.jvlc.2007.02.006
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发表时间：
2007-06-01
影响因子：
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通讯作者：
Kechadi, T.
中科院分区：
文献类型：
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作者：
Compieta, P.;Di Martino, S.;Kechadi, T.
时空数据集通常非常大，难以分析和显示。由于它们在许多应用环境中是决策支持的基础，因此最近出现了很多对数据挖掘技术的兴趣，以筛选出非常大的数据存储库的相关子集，以及可视化工具，以有效地显示结果。在本文中，我们提出了一个数据挖掘系统来处理非常大的时空数据集。在这一系统内，开发了新的技术，以有效地支持数据挖掘过程，处理数据集的空间和时间方面，并显示和解释结果。特别是，已经实现了两个互补的3D可视化环境。一个是利用Google Earth来显示与地图和其他地理层相结合的挖掘结果，而另一个是基于Java3D的工具，用于提供与非地理参考空间中的数据集的高级交互，例如显示关联规则和变量分布。(C)2007爱思唯尔有限公司保留所有权利。
Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mining system to deal with very large spatio-temporal data sets. Within this system, new techniques have been developed to efficiently support the data-mining process, address the spatial and temporal dimensions of the data set, and visualize and interpret results. In particular, two complementary 3D visualization environments have been implemented. One exploits Google Earth to display the mining outcomes combined with a map and other geographical layers, while the other is a Java3D-based tool for providing advanced interactions with the data set in a non-geo-referenced space, such as displaying association rules and variable distributions. (C) 2007 Elsevier Ltd. All rights reserved.