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Mining business databases

Authors:

Ronald J. Brachman,

Tom Khabaza,

Willi Kloesgen,

Gregory Piatetsky-Shapiro,

Evangelos SimoudisAuthors Info & Claims

Communications of the ACM, Volume 39, Issue 11

Pages 42 - 48

https://doi.org/10.1145/240455.240468

Published: 01 November 1996 Publication History

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Pinho CMoura AGaspar MNapolitano D(2022)Identificação de deficiências em textos educacionais com a aplicação de processamento de linguagem natural e aprendizado de máquinaETD - Educação Temática Digital10.20396/etd.v24i2.866006124:2(350-372)Online publication date: 23-May-2022
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Prakash Goteti L(2022)Design and Implementation of Item Based Collaborative Filtering -A Case Study2022 IEEE 7th International Conference on Recent Advances and Innovations in Engineering (ICRAIE)10.1109/ICRAIE56454.2022.10054282(83-86)Online publication date: 1-Dec-2022
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Index Terms

Mining business databases
1. Applied computing
  1. Enterprise computing
    1. Business process management
2. Information systems
  1. Data management systems
  2. Information systems applications
    1. Multimedia information systems

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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

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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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Efficient algorithms for mining high-utility itemsets in uncertain databases

Efficiently mining frequent itemsets from very large databases

Mining frequent itemsets in large databases: The hierarchical partitioning approach

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