Category Oriented Analysis for Visual Data Mining

Authors:
Hisako Shiohara;Yuichi Iizuka;T. Maruyama;Seiji Isobe
Affiliations:
-;-;-;-
Venue:
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Year:
1999

Citing 4
Cited 0

LinkWinds: interactive scientific data analysis and visualization

Communications of the ACM
Advances in knowledge discovery and data mining

Advances in knowledge discovery and data mining
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SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic Visualization Method for Visual Data Mining

PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining

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Abstract

Enterprises are now storing large amount of a data and data warehousing and data mining are gaining a great deal of attention for identifying effective business strategies. Data mining extracts effective patterns and rules from data warehouses automatically. Although various approaches have been attempted, we focus on visual data mining support to harness the perceptual and cognitive capabilities of the human user. The proposed visual data mining support system visualizes data using the rules or information induced by data mining algorithms. It helps users to acquire information. Although existing systems can extract data characteristics only from the complete data set, this paper proposes a category oriented analysis approach that can detect the features of the data of associated with one or more particular categories.