Visualization Techniques for Mining Large Databases: A Comparison

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Visualization Techniques for Mining Large Databases: A Comparison (1996)

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by Daniel A. Keim , Hans-Peter Kriege

Venue:	IEEE Transactions on Knowledge and Data Engineering
Citations:	116 - 1 self

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@ARTICLE{Keim96visualizationtechniques,
    author = {Daniel A. Keim and Hans-Peter Kriege},
    title = {Visualization Techniques for Mining Large Databases: A Comparison},
    journal = {IEEE Transactions on Knowledge and Data Engineering},
    year = {1996},
    volume = {8},
    pages = {923--938}
}

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Abstract

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 re...

Keyphrases

large database visualization technique visual data mining technique high potential exploratory data analysis new visualization-based approach cpu time high value second place first place stick figure visualization technique well-known visualization technique parallel coordinate basic idea many data item data count data value major goal multidimensional data secondary storage access

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