Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Better Interpretation of Numerical Data Sets by Relative and Absolute Typicality of Fuzzy Clustering Algorithms
Year:2009
Research Areas
  • Processing and signal analysis
Information
Abstract
In this work we take the concept of typicality from the cognitive and psychological point of view, and we apply their meaning to the interpretation of numerical data through fuzzy clustering algorithms. With the PFCM clustering algorithm, based on the Fuzzy c-Means clustering algorithm (FCM), we get a relative typicality (membership degree), and, also based on the Possibilitistic c- Means (PCM), an absolute typicality (typicality value). The results clearly show the advantages of the information obtained about the data set used, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used
International
Si
JCR
No
Title
Research on Computer Sciences S Issue: Advances in Pattern Recognition
ISBN
1870-4069
Impact factor JCR
0
Impact info
Volume
44
Journal number
0
From page
157
To page
166
Month
JUNIO
Ranking
Participants
  • Autor: Rubén Ruelas (UDG)
  • Autor: Diego Andina De la Fuente (UPM)
  • Autor: Maria G Corona-Nakamura (UDG)
  • Autor: Benjamín Ojeda Magaña (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Automatización en Señal y Comunicaciones (GASC)
  • Departamento: Señales, Sistemas y Radiocomunicaciones
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