Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
A comparison of clustering quality indices using outliers and noise
Year:2012
Research Areas
  • Artificial intelligence
Information
Abstract
Quality indices in clustering are used not only to assess the quality of the partitions but also to determine the number of clusters in the final result. When these indices are evaluated in a case study, real data conditions or different clustering algorithms are seldom taken into account. Here, some of the standard indices used in the literature are compared using more realistic databases that include outliers or noisy dimensions, which is more like a real problem-solving approach. Besides, three different clustering methods are used in an attempt to identify different behaviours. Also, the performance of the quality index-clustering algorithm tandem is compared to random grouping, with the aim of running an additional check. The indices are ranked, and index-based conclusions are drawn for all the scenarios.
International
Si
JCR
Si
Title
Intelligent Data Analysis
ISBN
1088-467X
Impact factor JCR
0,4
Impact info
Datos JCR del año 2010
Volume
16
10.3233/IDA-2012-0545
Journal number
From page
703
To page
715
Month
SIN MES
Ranking
Participants
  • Autor: Luis Pelayo Guerra Velasco (UPM)
  • Autor: Victor Robles Forcada (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
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