European Symposium on Principles of Data Mining and Knowledge Discovery

PKDD 1998: Principles of Data Mining and Knowledge Discovery pp 396-404

A procedure to compute prototypes for data mining in non-structured domains

  • J. Méndez
  • M. Hernández
  • J. Lorenzo
Posters

DOI: 10.1007/BFb0094843

Volume 1510 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Méndez J., Hernández M., Lorenzo J. (1998) A procedure to compute prototypes for data mining in non-structured domains. In: Żytkow J.M., Quafafou M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg

Abstract

This paper describes a technique for associating a set of symbols with an event in the context of knowledge discovery in database or data mining. The set of symbols is related to the keywords in a database which is used as an implicit knowledge source. The aim of this approach is to discover the significant keyword groups which best represent the event. A significant contribution of this work is a procedure which obtains the representative prototype of a group of symbolic data. It can be used for both, unsupervised learning to describe classes, and supervised learning to compute prototypes. The procedure involves defining an objective function and the subsequent hypothesis-exploring system and obtaining an advantageous procedure regarding computational costs.

Key words

learning data mining knowledge discovery symbolic clustering 

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Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • J. Méndez
    • 1
  • M. Hernández
    • 1
  • J. Lorenzo
    • 1
  1. 1.Dpto. de Informática y SistemasUniversidad de Las Palmas de Gran CanariaLas PalmasSpain