@inproceedings{Keim2003Visua-5655,
  year={2003},
  title={Visual Data Mining of Large Spatial Data Sets},
  number={2822},
  isbn={978-3-540-20111-3},
  publisher={Springer},
  address={Berlin [u.a.]},
  series={Lecture notes in computer science},
  booktitle={Databases in networked information systems : third International Workshop, DNIS 2003, Aizu, Japan, September 22 - 24, 2003},
  pages={201--215},
  editor={Bianchi-Berthouze, Nadia},
  author={Keim, Daniel A. and Panse, Christian and Sips, Mike}
}

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    <dc:contributor>Panse, Christian</dc:contributor>
    <dcterms:bibliographicCitation>First publ. in: Lecture notes in computer science, No 2822 (2003), pp. 201-215</dcterms:bibliographicCitation>
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    <dcterms:title>Visual Data Mining of Large Spatial Data Sets</dcterms:title>
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    <dc:contributor>Keim, Daniel A.</dc:contributor>
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    <dc:contributor>Sips, Mike</dc:contributor>
    <dcterms:abstract xml:lang="eng">Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually terabytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics etc. is, however, a very difficult task. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially the area of analyzing spatial data. We provide some examples for effective visualizations of spatial data in important application areas such as consumer analysis, e-mail traffic analysis, and census demographics.</dcterms:abstract>
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    <dcterms:issued>2003</dcterms:issued>
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