What Makes Patterns Interesting in Knowledge Discovery Systems.

@article{DBLP:journals/tkde/SilberschatzT96,
  author    = {Abraham Silberschatz and
               Alexander Tuzhilin},
  title     = {What Makes Patterns Interesting in Knowledge Discovery Systems},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {8},
  number    = {6},
  year      = {1996},
  pages     = {970-974},
  ee        = {db/journals/tkde/SilberschatzT96.html, db/journals/tkde/SilberschatzT96.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Copyright © 1996 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

CDROM Edition

References

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William J. Frawley, Gregory Piatetsky-Shapiro, Christopher J. Matheus: Knowledge Discovery in Databases: An Overview. Knowledge Discovery in Databases 1991: 1-30 BibTeX

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Jeffrey D. Ullman: Principles of Database and Knowledge-Base Systems, Volume I. Computer Science Press 1988, ISBN 0-7167-8158-1
Contents BibTeX

Referenced by

1. Flip Korn, Alexandros Labrinidis, Yannis Kotidis, Christos Faloutsos: Quantifiable Data Mining Using Ratio Rules. VLDB J. 8(3-4): 254-266(2000)
2. Themistoklis Palpanas: Knowledge Discovery in Data Warehouses. SIGMOD Record 29(3): 88-100(2000)
3. Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan: A Framework for Measuring Changes in Data Characteristics. PODS 1999: 126-137
4. Flip Korn, Alexandros Labrinidis, Yannis Kotidis, Christos Faloutsos: Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining. VLDB 1998: 582-593
5. Soumen Chakrabarti, Sunita Sarawagi, Byron Dom: Mining Surprising Patterns Using Temporal Description Length. VLDB 1998: 606-617
6. Tamas Abraham, John F. Roddick: Incremental Meta-Mining from Large Temporal Data Sets. ER Workshops 1998: 41-54