Visualization Techniques for Mining Large Databases : a Comparison

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KEIM, Daniel A., Hans-Peter KRIEGEL, 1996. Visualization Techniques for Mining Large Databases : a Comparison. In: IEEE transactions on knowledge and data engineering. 8(6), pp. 923-938. Available under: doi: 10.1109/69.553159

@article{Keim1996Visua-5669, title={Visualization Techniques for Mining Large Databases : a Comparison}, year={1996}, doi={10.1109/69.553159}, number={6}, volume={8}, journal={IEEE transactions on knowledge and data engineering}, pages={923--938}, author={Keim, Daniel A. and Kriegel, Hans-Peter} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:issued>1996</dcterms:issued> <dcterms:abstract xml:lang="eng">Visual data mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick figure visualization techniques. For the evaluation of visual data mining techniques, in the first place the perception of properties of the data counts, and only in the second place the CPU time and the number of secondary storage accesses are important. In addition to testing the visualization techniques using real data, we developed a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing environment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques.</dcterms:abstract> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:title>Visualization Techniques for Mining Large Databases : a Comparison</dcterms:title> <dcterms:available rdf:datatype="">2011-03-24T15:59:13Z</dcterms:available> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:hasPart rdf:resource=""/> <dc:creator>Kriegel, Hans-Peter</dc:creator> <dc:creator>Keim, Daniel A.</dc:creator> <dc:date rdf:datatype="">2011-03-24T15:59:13Z</dc:date> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dc:format>application/pdf</dc:format> <dc:contributor>Kriegel, Hans-Peter</dc:contributor> <dc:language>eng</dc:language> <dcterms:rights rdf:resource=""/> <dcterms:isPartOf rdf:resource=""/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <bibo:uri rdf:resource=""/> <dspace:hasBitstream rdf:resource=""/> <dcterms:bibliographicCitation>First publ. in: IEEE transactions on knowledge and data engineering 8 (1996), 6, pp. 923-938</dcterms:bibliographicCitation> </rdf:Description> </rdf:RDF>

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