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Zusammenfassung

Bei Analysen ist man häufig nicht an den ursprünglichen detaillierten Daten, sondern nur an Aussagen über die aggregierten Daten interessiert. In solchen Fällen ist eine kompakte Beschreibung einer gegebenen Datenmenge gesucht, d.h. eine deutlich kleinere Menge von Datensätzen mit Attributwerten auf abstrakterem Niveau. Dies ist die Data-Mining-Aufgabe der Generalisierung.

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Literatur

  • Carter C., Hamilton H. 1998, „Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases“, IEEE Transactions on Knowledge and Data Engineering, Vol.10, No.2, pp. 193–208.

    Article  Google Scholar 

  • Chaudhuri S., Dayal U. 1997, „An Overview of Data Warehousing and OLAP Technology“, ACM SIGMOD Record Vol. 26, No. 1, March 1997.

    Google Scholar 

  • Ester M., Wittmann R. 1998, „Incremental Generalization for Mining in a Data Warehousing Environment“, Proceedings Int. Conf. on Extending Database Technology, Valencia, Spain, pp. 135–149.

    Google Scholar 

  • Fernandez P. M., Schneider D. 1996, „The Ins and Outs (and everything in between) of Data Warehousing“, Tutorial Notes ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’96).

    Google Scholar 

  • Gray J., Bosworth A., Layman A., Pirahesh H. 1996, „Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tabs and Subtotals“, Proceedings 12th Int. Conf. on Data Engineering (DE ‘96), pp. 152–159.

    Google Scholar 

  • Han J., Cai Y., Cercone N. 1993, „Data-driven Discovery of Quantitative Rules in Relational Databases“, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 1, pp. 29–40.

    Article  Google Scholar 

  • Harinarayan V., Rajaraman A., Ullman J. D. 1996, „Implementing Data Cubes Efficiently“, Proceedings ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’96), pp. 205–216.

    Google Scholar 

  • Labio W., Quass D., Adelberg B. 1997, „Physical Database Design for Data Warehousing“, Proceedings 13th Int. Conf. on Data Engineering (DE‘97), pp. 277–288.

    Google Scholar 

  • Shoshani A. 1997, „OLAP and Statistical Databases: Similarities and Differences“, Proceedings ACM SIGMOD Int. Conf. on Principles of Database Systems (PODS ‘87), pp. 185–196.

    Google Scholar 

  • TPCD 1998, „TPC Benchmark D“, Transaction Processing Council (TPC), http://www.tpc.org.

    Google Scholar 

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© 2000 Springer-Verlag Berlin Heidelberg

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Ester, M., Sander, J. (2000). Generalisierung. In: Knowledge Discovery in Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58331-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-58331-5_6

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