Ensembles for unsupervised outlier detection: challenges and research questions (2013)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Source:
- Título: SIGKDD Explorations
- ISSN: 1931-0145
- Volume/Número/Paginação/Ano: v. 15, n. 1, p. 11-22, jun. 2013
-
ABNT
ZIMEK, Arthur e CAMPELLO, Ricardo José Gabrielli Barreto e SANDER, Jörg. Ensembles for unsupervised outlier detection: challenges and research questions. SIGKDD Explorations, v. 15, n. ju 2013, p. 11-22, 2013Tradução . . Disponível em: http://www.kdd.org/newsletter/explorations-june-2013-15-1. Acesso em: 12 jan. 2026. -
APA
Zimek, A., Campello, R. J. G. B., & Sander, J. (2013). Ensembles for unsupervised outlier detection: challenges and research questions. SIGKDD Explorations, 15( ju 2013), 11-22. Recuperado de http://www.kdd.org/newsletter/explorations-june-2013-15-1 -
NLM
Zimek A, Campello RJGB, Sander J. Ensembles for unsupervised outlier detection: challenges and research questions [Internet]. SIGKDD Explorations. 2013 ; 15( ju 2013): 11-22.[citado 2026 jan. 12 ] Available from: http://www.kdd.org/newsletter/explorations-june-2013-15-1 -
Vancouver
Zimek A, Campello RJGB, Sander J. Ensembles for unsupervised outlier detection: challenges and research questions [Internet]. SIGKDD Explorations. 2013 ; 15( ju 2013): 11-22.[citado 2026 jan. 12 ] Available from: http://www.kdd.org/newsletter/explorations-june-2013-15-1 - A cluster based hybrid feature selection approach
- Communities validity: methodical evaluation of community mining algorithms
- Efficient computation of multiple density-based clustering hierarchies
- Relative validity criteria for community mining algorithms
- Identificação e controle de processos via desenvolvimentos em séries ortonormais: parte B: controle preditivo
- A fuzzy extension of the rand index and other related indexes for clustering and classification assessment
- On the selection of appropriate distances for gene expression data clustering
- Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis
- Clustering of RNA-Seq samples: comparison study on cancer data
- Model selection for semi-supervised clustering
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
