Mining Knowledge at Multiple Concept Levels.

Jiawei Han: Mining Knowledge at Multiple Concept Levels. CIKM 1995: 19-24

@inproceedings{DBLP:conf/cikm/Han95,
  author    = {Jiawei Han},
  title     = {Mining Knowledge at Multiple Concept Levels},
  booktitle = {CIKM '95, Proceedings of the 1995 International Conference on
               Information and Knowledge Management, November 28 - December
               2, 1995, Baltimore, Maryland, USA},
  publisher = {ACM},
  year      = {1995},
  pages     = {19-24},
  ee        = {db/conf/cikm/Han95.html, http://doi.acm.org/10.1145/221270.221287},
  crossref  = {DBLP:conf/cikm/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Most studies on data mining have been focused at mining rules at single concept levels, i.e,, either at the primitive level or at a rather high concept level. However, it is often desirable to discover knowledge at multiple concept levels. Mining knowledge at multiple levels may help database users find some interesting rules which are difficult to be discovered otherwise and view database contents at different abstraction levels and from different angles. Methods for mining knowledge at multiple concept levels can often be developed by extension of existing data mining techniques. Moreover, for eficient processing and interactive mining of multiple-level rules, it is often necessary to adopt techniques such as step-by-step generalization/specialization or progressive deepening of a knowledge mining process. Other issues, such as visual representation of knowledge at multiple levels, and redundant rule filtering, should also be studied in depth.

Copyright © 1995 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

ACM SIGMOD Anthology

CDROM Version: Load the CDROM "Volume 2 Issue 4, CIKM, DOLAP, GIS, SIGFIDET, ..." and ...

Windows: Click the letter of your CD drive
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Mac: Click here
UNIX/LINUX: mount the CD and click on the path of your mount point:
/Anthology/An2-4 or /cdrom

DVD Version: Load ACM SIGMOD Anthology DVD 1" and ...

Windows: Click the letter of your CD drive
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Mac: Click here
UNIX/LINUX: mount the DVD and click on the path of your mount point:
/Anthology/aDVD1 or /dvd

Printed Edition

CIKM '95, Proceedings of the 1995 International Conference on Information and Knowledge Management, November 28 - December 2, 1995, Baltimore, Maryland, USA. ACM 1995
Contents

Online Edition

Citation Page

References

[1]: Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216
[2]: Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499
[3]: Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, Ramasamy Uthurusamy (Eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996, ISBN 0-262-56097-6
Contents
[4]: Douglas H. Fisher: Improving Inference through Conceptual Clustering. AAAI 1987: 461-465
[5]: Jiawei Han, Yandong Cai, Nick Cercone: Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Trans. Knowl. Data Eng. 5(1): 29-40(1993)
[6]: Jiawei Han, Yongjian Fu: Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases. KDD Workshop 1994: 157-168
[7]: Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431
[8]: Jiawei Han, Yongjian Fu: Attribute-Oriented Induction in data Mining. Advances in Knowledge Discovery and Data Mining 1996: 399-421
[9]: ...
[10]: Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo: Finding Interesting Rules from Large Sets of Discovered Association Rules. CIKM 1994: 401-407
[11]: Krzysztof Koperski, Jiawei Han: Discovery of Spatial Association Rules in Geographic Information Databases. SSD 1995: 47-66
[12]: Heikki Mannila, Kari-Jouko Räihä: Dependency Inference. VLDB 1987: 155-158
[13]: ...
[14]: Jong Soo Park, Ming-Syan Chen, Philip S. Yu: An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175-186
[15]: Gregory Piatetsky-Shapiro: Discovery, Analysis, and Presentation of Strong Rules. Knowledge Discovery in Databases 1991: 229-248
[16]: Gregory Piatetsky-Shapiro, William J. Frawley (Eds.): Knowledge Discovery in Databases. AAAI/MIT Press 1991, ISBN 0-262-62080-4
Contents
[17]: J. Ross Quinlan: Induction of Decision Trees. Machine Learning 1(1): 81-106(1986)
[18]: J. Ross Quinlan: C4.5: Programs for Machine Learning. Morgan Kaufmann 1993, ISBN 1-55860-238-0
[19]: Wei-Min Shen, KayLiang Ong, Bharat G. Mitbander, Carlo Zaniolo: Metaqueries for Data Mining. Advances in Knowledge Discovery and Data Mining 1996: 375-398
[20]: Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419

Referenced by

Holger Günzel, Jens Albrecht, Wolfgang Lehner: Data Mining in a Multidimensional Environment. ADBIS 1999: 191-204
Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)