Publikation: Visual data mining in large geospatial point sets
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Visual data-mining techniques have proven valuable in exploratory data analysis, and they have strong potential in the exploration of large databases. Detecting interesting local patterns in large data sets is a key research challenge. Particularly challenging today is finding and deploying efficient and scalable visualization strategies for exploring large geospatial data sets. One way is to share ideas from the statistics and machine-learning disciplines with ideas and methods from the information and geo-visualization disciplines. PixelMaps in the Waldo system demonstrates how data mining can be successfully integrated with interactive visualization. The increasing scale and complexity of data analysis problems require tighter integration of interactive geospatial data visualization with statistical data-mining algorithms.
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KEIM, Daniel A., Christian PANSE, Mike SIPS, Stephen C. NORTH, 2004. Visual data mining in large geospatial point sets. In: IEEE Computer Graphics and Applications. 2004, 24(5), pp. 36-44. ISSN 0272-1716. eISSN 1558-1756. Available under: doi: 10.1109/MCG.2004.41BibTex
@article{Keim2004-09Visua-40556, year={2004}, doi={10.1109/MCG.2004.41}, title={Visual data mining in large geospatial point sets}, number={5}, volume={24}, issn={0272-1716}, journal={IEEE Computer Graphics and Applications}, pages={36--44}, author={Keim, Daniel A. and Panse, Christian and Sips, Mike and North, Stephen C.} }
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