loading...
Temporal Knowledge Discovery for Multivariate Time Series with Enhanced Self-Organizing Maps
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859391IEEE-INNS-ENNS International Joint Co ...
 This Article 
 
PDF
HTML
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
G. Guimarães, Universidade Nova de Lisboa
This paper presents enhanced Self-Organizing Maps (SOMs) for exploratory multivariate time series analysis in the context of temporal data mining. The main idea lies in an adequate combination of approaches with SOMs for temporal processing. It is part of a recently developed method that introduces several abstraction levels for temporal knowledge conversion. The method provides a conversion of discovered temporal patterns in multivariate time series with enhanced SOMs into a linguistic knowledge representation, in form of temporal grammatical rules. This method was successfully applied to a problem in medicine. Even some previously unknown knowledge was found.
Citation:
G. Guimarães, "Temporal Knowledge Discovery for Multivariate Time Series with Enhanced Self-Organizing Maps," ijcnn, vol. 6, pp.6165, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000
Usage of this product signifies your acceptance of the Terms of Use.