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Abstract: An important issue in data mining is the recognition of complex dependencies between attributes. Past techniques for identifying attribute dependence include correlation coefficients, scatterplots, and equiwidth histograms. These techniques are sensitive to outliers, and often are not sufficiently informative to identify the kind of attribute dependence present. We propose a new approach, which we call independence diagrams. We divide each attribute into ranges; for each pair of attributes, the ... (Update)
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...no standard mapping into the Cartesian coordinate system. Recently, several techniques of visual data mining have been introduced. BJR 98] presents the technique of Independence Diagrams for visualizing dependencies between two attributes. The brightness of a cell in the two...
.... survey of such data reduction techniques is presented in [13] Techniques for visualizing large multidimensional datasets are presented in [14]. Our techniques enable one to visually identify, for any pair of dimensions, regions where the two dimensional distribution is not...
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BibTeX entry: (Update)
Berchtold S., Jagadish H.V., Ross K.A.: "Independence Diagrams: A Technique for Visual Data Mining", Proc. 4th Intl. Conf. on Knowledge Discovery and Data Mining, New York City, 1998, pp. 139-143. http://citeseer.ist.psu.edu/berchtold98independence.html More
@inproceedings{ berchtold98independence,
author = "Stefan Berchtold and H. V. Jagadish and Kenneth A. Ross",
title = "Independence Diagrams: {A} Technique for Visual Data Mining",
booktitle = "Proc. 4th Int. Conf. Knowledge Discovery and Data Mining, {KDD}",
month = "27--31~",
publisher = "AAAI Press",
editor = "Rakesh Agrawal and Paul E. Stolorz and Gregory Piatetsky-Shapiro",
isbn = "1-57735-070-7",
pages = "139--143",
year = "1998",
url = "citeseer.ist.psu.edu/berchtold98independence.html" }
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