A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets (2012)
- Authors:
- Autor USP: BATISTA, GUSTAVO ENRIQUE DE ALMEIDA PRADO ALVES - ICMC
- Unidade: ICMC
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: SIAM
- Publisher place: Philadelphia
- Date published: 2012
- ISBN: 9781611972320
- Source:
- Título: Proceedings
- Conference titles: SIAM International Conference on Data Mining
-
ABNT
QIANG, Zhu et al. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets. 2012, Anais.. Philadelphia: SIAM, 2012. Disponível em: http://siam.omnibooksonline.com/2012datamining/. Acesso em: 27 dez. 2025. -
APA
Qiang, Z., Batista, G. E. de A. P. A., Rakthanmanon, T., & Keogh, E. (2012). A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets. In Proceedings. Philadelphia: SIAM. Recuperado de http://siam.omnibooksonline.com/2012datamining/ -
NLM
Qiang Z, Batista GE de APA, Rakthanmanon T, Keogh E. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets [Internet]. Proceedings. 2012 ;[citado 2025 dez. 27 ] Available from: http://siam.omnibooksonline.com/2012datamining/ -
Vancouver
Qiang Z, Batista GE de APA, Rakthanmanon T, Keogh E. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets [Internet]. Proceedings. 2012 ;[citado 2025 dez. 27 ] Available from: http://siam.omnibooksonline.com/2012datamining/ - Contribuições em mineração de dados temporais e classes desbalanceadas
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- Mineração de séries temporais por meio da extração de características e da identificação de motifs
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- Towards automatic classification on flying insects using inexpensive sensors
- Distância invariante à complexidade baseada em dimensão fractal para classificação de séries temporais
- Data mining a trillion time series subsequences under dynamic time warping
- An empirical comparison of dissimilarity measures for time series classification
- Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model
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