Differences
This shows you the differences between two versions of the page.
Next revision | Previous revision Next revision Both sides next revision | ||
cap241:2018:tsclustering [2018/08/22 16:12] karine created |
cap241:2018:tsclustering [2018/08/22 17:23] karine |
||
---|---|---|---|
Line 1: | Line 1: | ||
+ | {{:cap241:2018:curso_logo.png?900}} | ||
+ | |||
===== Satellite Image Time Series Clustering using SOM ==== | ===== Satellite Image Time Series Clustering using SOM ==== | ||
Essa página contém o material do minicurso "Satellite Image Time Series Clustering using SOM" ministrado no [[http://www.inpe.br/worcap/2018/|Worcap 2018]]. | Essa página contém o material do minicurso "Satellite Image Time Series Clustering using SOM" ministrado no [[http://www.inpe.br/worcap/2018/|Worcap 2018]]. | ||
+ | * Carga horária: 2 horas | ||
+ | * Instrutores: Karine Reis Ferreira e Lorena Santos | ||
+ | * Local: LIT - INPE | ||
+ | * Data: 23 de Agosto de 2018 | ||
+ | |||
+ | ===== Parte prática: ambiente computacional ===== | ||
+ | |||
+ | O curso terá uma parte prática utilizando R. Os inscritos devem trazer seus próprios notebooks com os seguintes softwares instalados: | ||
+ | - [[https://www.rstudio.com/products/rstudio/download/|RStudio Desktop - versão >= 1.0.153]] | ||
+ | - [[http://nbcgib.uesc.br/mirrors/cran/|R - versão >= 3.3.3]] | ||
+ | |||
+ | Depois de instalado o R e o RStudio, os seguintes pacotes do R devem ser instalados: | ||
+ | - zoo | ||
+ | - kohonen | ||
+ | - dplyr | ||
+ | - ggplot2 | ||
+ | - stats | ||
+ | - proxy | ||
+ | - dendextend | ||
+ | - TSdist | ||
+ | - dtw | ||
+ | |||
+ | Para instalar todos os pacotes acima de uma vez só, utilize o comando abaixo: | ||
+ | |||
+ | install.packages(c("zoo","kohonen","dplyr", "ggplot2", "stats", "proxy","dendextend","TSdist", "dtw"), dependencies = TRUE) | ||
+ | |||
+ | Todos os pacotes estão disponibilizados no CRAN! | ||
+ | |||
+ | ===== Material do curso ===== | ||
+ | * Parte teórica | ||
+ | * Parte prática | ||
+ | * R scripts | ||
+ | |||
+ | ===== Referências Bibliográficas ===== | ||
+ | * **(Aghabozorgi et al. 2015)** Aghabozorgi, Saeed, Ali Seyed Shirkhorshidi, and Teh Ying Wah. "Time-series clustering–A decade review." Information Systems. 53 (2015): 16-38 | ||
+ | * **(Ding et al. 2008)** Ding, Hui, et al. "Querying and mining of time series data: experimental comparison of representations and distance measures." Proceedings of the VLDB Endowment 1.2 (2008): 1542-1552. | ||
+ | * **(Esling and Agon, 2012)** Esling, Philippe, and Carlos Agon. "Time-series data mining." ACM Computing Surveys (CSUR) 45.1 (2012): 12. | ||
+ | * **(Giogino, 2009)** Giorgino, Toni. "Computing and visualizing dynamic time warping alignments in R: the dtw package." Journal of statistical Software 31.7 (2009): 1-24. | ||
+ | * **(Keogh and Lin, 2003)** Keogh, Eamonn, and Jessica Lin. "Clustering of time-series subsequences is meaningless: implications for previous and future research." Knowledge and information systems 8.2 (2005): 154-177. | ||
+ | * **(Keogh and Ratanamahatana, 2005)** Keogh, Eamonn, and Chotirat Ann Ratanamahatana. “Exact indexing of dynamic time warping” Knowledge and information systems 7.3 (2005): 358-386. | ||
+ | * **(Mori et al. 2016)** Mori, Usue, Alexander Mendiburu, and Jose A. Lozano. "Distance measures for time series in R: The TSdist package." R J. 8 (2016): 451-459. | ||
+ | * **(Samarasinghe, 2016)** Samarasinghe, Sandhya. “Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition.” Auerbach publications, (2016). | ||
+ | |||
+ |