空间数据挖掘的实践:原则和案例研究

Christine Kopp, D. Hecker, Maike Krause-Traudes, M. May, S. Scheider, Daniel Schulz, Hendrik Stange, S. Wrobel
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引用次数: 2

摘要

在地理空间中,几乎任何数据都可以被引用。这些数据允许利用空间中物体的位置和关系以及地理背景信息进行高级分析。尽管空间数据挖掘仍然是一门年轻的研究学科,但过去几年的研究进展表明,当空间方面被视为数据挖掘和模型构建的一个组成部分时,空间数据的特殊挑战是可以被掌握的,并且该技术已经准备好用于实际应用。特别是在本章中,我们详细描述了我们执行的几个客户项目,这些项目都涉及针对业务相关任务的定制数据挖掘解决方案。应用范围从客户细分到交通频率预测和GPS轨迹分析。他们被选中展示关键挑战,提供先进的解决方案,并引发进一步的研究问题。
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Spatial Data Mining in Practice: Principles and Case Studies
Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.
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