Hartung-Gorre Verlag

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D-78465 Konstanz

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Series in Computer Science
Edited by Thomas M. Stricker

Vol. 8

Mike Sips,
Pixel-based Visual Data Mining
in Large Geo-Spatial Point Sets

2005; 231 pages/Seiten, € 64,00. ISBN 3-86628-048-3






The information revolution is creating and publishing vast data sets, such as records of business transactions, environmental statistics and census demographics. In many application domains, this data is collected and indexed by geo-spatial location. The discovery of interesting patterns in such databases through spatial data mining is a key to turning this raw data into valuable information. Challenges arise because newly available geo-spatial data sets often have millions of records, or even far more. New techniques are needed to cope with this scale.
Our Wide Area Layout Data Observer (WALDO) is a novel visual data mining system, based on PixelMaps, for analyzing large geo-spatial data sets. PixelMaps combine density-based distortion of map regions with local pixel repositioning to highlight clusters and avoid data loss from overplotting.

Keywords: vast data sets, databases, geo-spatial location, Wide Area Layout Data Observer (WALDO), PixelMaps, data loss, spatial data mining, visual data mining

Series in Computer Science

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