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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/76645
Title: 
Metrics to support the evaluation of association rule clustering
Author(s): 
Institution: 
  • Universidade Estadual Paulista (UNESP)
  • Universidade de São Paulo (USP)
ISSN: 
  • 0302-9743
  • 1611-3349
Abstract: 
Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.
Issue Date: 
26-Sep-2013
Citation: 
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259.
Time Duration: 
248-259
Keywords: 
  • Association Rules
  • Clustering
  • Pre-processing
  • Association mining
  • Pre-processing step
  • Research communities
  • Suitable solutions
  • Data warehouses
  • Association rules
Source: 
http://dx.doi.org/10.1007/978-3-642-40131-2_21
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/76645
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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