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The KDD process for extracting useful knowledge from volumes of data

Authors:

Usama Fayyad,

Gregory Piatetsky-Shapiro,

Padhraic SmythAuthors Info & Claims

Communications of the ACM, Volume 39, Issue 11

Pages 27 - 34

https://doi.org/10.1145/240455.240464

Published: 01 November 1996 Publication History

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References

[1]

Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, I. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI/MIT Press, Cambridge, Mass., 1996.

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[2]

Elder, J., and Pregibon, D. A statistical perspective on KDD. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI/MIT Press, Cambridge, Mass., 1996.

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Fayyad, U., and Uthurusamy, R., Eds. Proceedings of KDD-95: The First International Conference on Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, Calif., 1995.

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[4]

Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI/MIT Press, Cambridge, Mass., 1996.

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[5]

Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., Eds. Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Cambridge, Mass., 1996.

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[6]

Heckerman, D. Bayesian networks for knowledge discovery. Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI/MIT Press, Cambridge, Mass., 1996.

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Langley, P., and Simon, H.A. Applications of machine learning and rule induction. Commun. ACM 38, 11 (Nov. 1995), 55-64.

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Shameem AVisvesvaran CRevathi VJain ASinghal RDhanraj J(2024)Supply Chain Security in 6G Networks6G Security Education and Multidisciplinary Implementation10.4018/979-8-3693-7421-4.ch011(191-211)Online publication date: 28-Jun-2024
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. GSaini DKaur SSingh M(2024)Machine Learning and Big Data Analytics for Precision Cardiac RiskStratification and Heart DiseasesInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24FEB155(2041-2046)Online publication date: 18-Apr-2024
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Index Terms

The KDD process for extracting useful knowledge from volumes of data
1. Information systems
  1. Data management systems
  2. Information systems applications
    1. Multimedia information systems
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning
        Software design techniques
    2. Software development process management

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Published In

Communications of the ACM Volume 39, Issue 11

Nov. 1996

82 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/240455

Editor:
Diane Crawford

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 November 1996

Published in CACM Volume 39, Issue 11

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https://doi.org/10.4018/979-8-3693-7421-4.ch011
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https://doi.org/10.4018/978-1-6684-7366-5.ch048
. GSaini DKaur SSingh M(2024)Machine Learning and Big Data Analytics for Precision Cardiac RiskStratification and Heart DiseasesInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24FEB155(2041-2046)Online publication date: 18-Apr-2024
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Extracting useful knowledge from event logs

Ontology-Based Knowledge Model for Multi-View KDD Process

KDD process planning

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