article

Free access

DBMiner: interactive mining of multiple-level knowledge in relational databases

Authors:

Krzysztof KoperskiAuthors Info & Claims

ACM SIGMOD Record, Volume 25, Issue 2

Page 550

https://doi.org/10.1145/235968.280356

Published: 01 June 1996 Publication History

PDF eReader

Abstract

Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.

References

[1]

J. Han, Y. Cai, and N. Cercone. Data-driven discovery of quantitative rules in relational databases. IEEE Trans. Knowledge and Data Engineering, 5:29-40, 1993.

Digital Library

Google Scholar

[2]

J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proc. VLDB'95, pp. 420- 431, Zurich, Switzerland, Sept. 1995.

Digital Library

Google Scholar

[3]

J. Han and Y. Fu. Exploration of the power of attributeoriented induction in data mining. In U.M. Fayyad, et al. (eds.), Advances in Knowledge D~scovery and Data Mining, pp. 399-421. AAAI/MIT Press, 1996.

Crossref

Google Scholar

Cited By

View all

Mehenni T(2018)Geographic Knowledge Discovery in Multiple Spatial DatabasesInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch008(121-143)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch008
Mehenni T(2017)Geographic Knowledge Discovery in Multiple Spatial DatabasesHandbook of Research on Geographic Information Systems Applications and Advancements10.4018/978-1-5225-0937-0.ch013(344-366)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0937-0.ch013
Winarko ERoddick J(2008)A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2008.2020:6(825-835)Online publication date: 1-Jun-2008
https://dl.acm.org/doi/10.1109/TKDE.2008.20
Show More Cited By

Index Terms

DBMiner: interactive mining of multiple-level knowledge in relational databases
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Relational database model
    2. Query languages
      1. Relational database query languages
  2. Information systems applications

Recommendations

DBMiner: a system for data mining in relational databases and data warehouses
CASCON '97: Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative research

A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, ...
DBMiner: interactive mining of multiple-level knowledge in relational databases
SIGMOD '96: Proceedings of the 1996 ACM SIGMOD international conference on Management of data

Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization,...
DBMiner: a system for mining knowledge in large relational databases
KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining

A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, ...

Comments

Information & Contributors

Information

Published In

ACM SIGMOD Record Volume 25, Issue 2

June 1996

557 pages

ISSN:0163-5808

DOI:10.1145/235968

Chairman:
T. H. Merrett
McGill Univ.
,
Editors:
H. V. Jagadish,
Inderpal Singh Mumick

Issue’s Table of Contents

SIGMOD '96: Proceedings of the 1996 ACM SIGMOD international conference on Management of data
June 1996
560 pages
ISBN:0897917944
DOI:10.1145/233269
Editor:
Jennifer Widom
Stanford Univ., Stanford, CT

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1996

Published in SIGMOD Volume 25, Issue 2

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

44
Total Citations
View Citations
651
Total Downloads

Downloads (Last 12 months)136
Downloads (Last 6 weeks)74

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mehenni T(2018)Geographic Knowledge Discovery in Multiple Spatial DatabasesInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch008(121-143)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch008
Mehenni T(2017)Geographic Knowledge Discovery in Multiple Spatial DatabasesHandbook of Research on Geographic Information Systems Applications and Advancements10.4018/978-1-5225-0937-0.ch013(344-366)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0937-0.ch013
Winarko ERoddick J(2008)A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2008.2020:6(825-835)Online publication date: 1-Jun-2008
https://dl.acm.org/doi/10.1109/TKDE.2008.20
Ordonez C(2006)Integrating K-Means Clustering with a Relational DBMS Using SQLIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2006.3118:2(188-201)Online publication date: 1-Feb-2006
https://dl.acm.org/doi/10.1109/TKDE.2006.31
Gupta SBhatnagar VWasan S(2005)Architecture for knowledge discovery and knowledge managementKnowledge and Information Systems10.1007/s10115-004-0153-x7:3(310-336)Online publication date: 1-Mar-2005
https://doi.org/10.1007/s10115-004-0153-x
Morzy TWojciechowski MZakrzewicz M(2001)Data Mining Support in Database Management SystemsData Warehousing and Knowledge Discovery10.1007/3-540-44466-1_38(382-392)Online publication date: 6-Jul-2001
https://doi.org/10.1007/3-540-44466-1_38
Purohit D(2023)Semantic and�Efficient Symbolic Learning over�Knowledge GraphsThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_43(244-254)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-3-031-43458-7_43
Sharma RKaushik MPeious SBazin AShah SFister IYahia SDraheim D(2022)A Novel Framework for Unification of Association Rule Mining, Online Analytical Processing and Statistical ReasoningIEEE Access10.1109/ACCESS.2022.3142537(1-1)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3142537
Sharma RKaushik MPeious SShahin MYadav ADraheim D(2022)Towards Unification of Statistical Reasoning, OLAP and Association Rule Mining: Semantics and PragmaticsDatabase Systems for Advanced Applications10.1007/978-3-031-00123-9_48(596-603)Online publication date: 8-Apr-2022
https://doi.org/10.1007/978-3-031-00123-9_48
Jentner WKeim D(2019)Visualization and Visual Analytic Techniques for PatternsHigh-Utility Pattern Mining10.1007/978-3-030-04921-8_12(303-337)Online publication date: 19-Jan-2019
https://doi.org/10.1007/978-3-030-04921-8_12
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

DBMiner: a system for data mining in relational databases and data warehouses