大空间数据集选择性可视化的数据挖掘

S. Shekhar, Chang-Tien Lu, Pusheng Zhang, Rulin Liu
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引用次数: 28

摘要

数据挖掘是从大量数据中提取隐含的、有价值的和有趣的信息的过程。可视化是可视化地探索数据以进行模式和趋势分析的过程,是浏览空间数据集查找模式的常用方法。然而,随着空间数据集的不断增长,人们很难全面浏览这些数据集,需要数据挖掘算法来过滤掉空间数据集中大量无趣的部分。我们构建了一个基于网络的可视化软件包,用于观察空间模式和时间趋势的总结。我们还提出了数据挖掘算法,用于过滤出空间异常模式的大量数据集。这些算法在现实世界的明尼阿波利斯-圣路易斯市进行了实现和测试。保罗(双子城)交通数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mining for selective visualization of large spatial datasets
Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety, and data mining algorithms are needed to filter out large uninteresting parts of spatial datasets. We construct a web-based visualization software package for observing the summarization of spatial patterns and temporal trends. We also present data mining algorithms for filtering out vast parts of datasets for spatial outlier patterns. The algorithms were implemented and tested with a real-world set of Minneapolis-St. Paul (Twin Cities) traffic data.
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