Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42883
Title: A procedure to compute prototypes for data mining in non-structured domains
Authors: Mendez, J 
Hernández, M. 
Lorenzo, J. 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Learning
Data mining
Knowledge discovery
Symbolic clustering
Issue Date: 1998
Journal: Lecture Notes in Computer Science 
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.
URI: http://hdl.handle.net/10553/42883
ISBN: 3-540-65068-7
ISSN: 0302-9743
DOI: 10.1007/BFb0094843
Source: 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
Appears in Collections:Actas de congresos
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