research-article

Systems for Knowledge Discovery in Databases

Authors:

G. Piatetsky-ShapiroAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 5, Issue 6

Pages 903 - 913

https://doi.org/10.1109/69.250073

Published: 01 December 1993 Publication History

Abstract

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research.

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 5, Issue 6

December 1993

185 pages

ISSN:1041-4347

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Copyright © Copyright © 1993 IEEE. All Rights Reserved.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 December 1993

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Cited By

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Sağ TKahramanlı Örnek H(2022)Classification rule mining based on Pareto-based Multiobjective OptimizationApplied Soft Computing10.1016/j.asoc.2022.109321127:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109321
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Sarkar BSana S(2009)A hybrid approach to design efficient learning classifiersComputers & Mathematics with Applications10.1016/j.camwa.2009.01.03858:1(65-73)Online publication date: 1-Jul-2009
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