An Interval Classifier for Database Mining Applications.

Rakesh Agrawal, Sakti P. Ghosh, Tomasz Imielinski, Balakrishna R. Iyer, Arun N. Swami: An Interval Classifier for Database Mining Applications. VLDB 1992: 560-573

@inproceedings{DBLP:conf/vldb/AgrawalGIIS92,
  author    = {Rakesh Agrawal and
               Sakti P. Ghosh and
               Tomasz Imielinski and
               Balakrishna R. Iyer and
               Arun N. Swami},
  editor    = {Li-Yan Yuan},
  title     = {An Interval Classifier for Database Mining Applications},
  booktitle = {18th International Conference on Very Large Data Bases, August
               23-27, 1992, Vancouver, Canada, Proceedings},
  publisher = {Morgan Kaufmann},
  year      = {1992},
  isbn      = {1-55860-151-1},
  pages     = {560-573},
  ee        = {db/conf/vldb/AgrawalGIIS92.html},
  crossref  = {DBLP:conf/vldb/92},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We are given a large population database that contains information about population instances. The population is known to comprise of m groups, but the population instances are not labeled with the group identification. Also given is a population sample (much smaller than the population but representative of it) in which the group labels of the instances are known. We present an interval classifier (IC) which generates a classification function for each group that can be used to efficiently retrieve all instances of the specified group from the population database. To allow IC to be embedded in interactive loops to answer adhoc queries about attributes with missing values, IC has been designed to be efficient in the generation of classification functions. Preliminary experimental results indicate that IC not only has retrievaland classifier generation efficiency advantages, but also compares favorably inthe classification accuracy with current tree classifiers, such as ID3, which were primarily designed for minimizing classification errors. We also describe some new applications that arise from encapsulating the classification capability in database systems and discuss extensions to IC forit to be used in these new application domains.

Copyright © 1992 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

Online Paper

Download PDF file (www.vldb.org, Darmstadt, Germany)
Download PDF file (www.acm.org, New York, USA)

ACM SIGMOD Anthology

CDROM Version: Load the CDROM "Volume 1 Issue 5, VLDB '89-'97" 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/vldb8997 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

Li-Yan Yuan (Ed.): 18th International Conference on Very Large Data Bases, August 23-27, 1992, Vancouver, Canada, Proceedings. Morgan Kaufmann 1992, ISBN 1-55860-151-1
Contents

References

[1]: Dina Bitton, David J. DeWitt, Carolyn Turbyfill: Benchmarking Database Systems A Systematic Approach. VLDB 1983: 8-19
[2]: Leo Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone: Classification and Regression Trees. Wadsworth 1984, ISBN 0-534-98053-8
[3]: ...
[4]: ...
[5]: ...
[6]: ...
[7]: ...
[8]: Laurent Hyafil, Ronald L. Rivest: Constructing Optimal Binary Decision Trees is NP-Complete. Inf. Process. Lett. 5(1): 15-17(1976)
[9]: Anil K. Jain, Richard C. Dubes: Algorithms for Clustering Data. Prentice-Hall 1988
[10]: Ravi Krishnamurthy, Tomasz Imielinski: Research Directions in Knowledge Discovery. SIGMOD Record 20(3): 76-78(1991)
[11]: ...
[12]: ...
[13]: J. Ross Quinlan: Induction of Decision Trees. Machine Learning 1(1): 81-106(1986)
[14]: ...
[15]: J. Ross Quinlan, Ronald L. Rivest: Inferring Decision Trees Using the Minimum Description Length Principle. Inf. Comput. 80(3): 227-248(1989)
[16]: ...
[17]: Gregory Piatetsky-Shapiro, William J. Frawley (Eds.): Knowledge Discovery in Databases. AAAI/MIT Press 1991, ISBN 0-262-62080-4
Contents
[18]: ...
[19]: ...
[20]: Shalom Tsur: Data Dredging. IEEE Data Eng. Bull. 13(4): 58-63(1990)