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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}
}
BibTeX

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.

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

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Contents BibTeX
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Referenced by

  1. Holger Günzel, Jens Albrecht, Wolfgang Lehner: Data Mining in a Multidimensional Environment. ADBIS 1999: 191-204
  2. 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)
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