19.1 Clustering Setup
THIS SECTION IS UNDER DEVELOPMENT. PLEASE CHECK BACK LATER
20200902 The R packages used in this chapter include biclust (Kaiser et al. 2021).
# Load required packages from local library into the R session.
library(biclust) # Bicluster analysis.
library(dplyr) # Wrangling: glimpse() group_by() print() select() mutate().
library(rattle) # Weather dataset.
The rattle::weatherAUS dataset is loaded into the template
variable ds
and further template variables are setup as
introduced by Graham J. Williams (2017). See
Chapter 8 for details.
<- "weatherAUS"
dsname <- get(dsname)
ds
<- nrow(ds)
nobs
<- names(ds)
vnames %<>% clean_names(numerals="right")
ds names(vnames) <- names(ds)
<- names(ds)
vars <- "rain_tomorrow"
target <- c(target, vars) %>% unique() %>% rev() vars
It is always useful to remind ourselves of the dataset with a random sample:
%>% sample_frac() %>% select(date, location, sample(3:length(vars), 5)) ds
## # A tibble: 226,868 × 7
## date location wind_speed_3pm min_temp humidity_9am evaporation
## <date> <chr> <dbl> <dbl> <int> <dbl>
## 1 2015-03-22 Cobar 11 16.9 56 8.6
## 2 2019-05-15 Bendigo 19 5 97 NA
## 3 2020-02-16 NorahHead 28 20.4 89 NA
## 4 2022-02-08 CoffsHarbour 17 17.3 62 NA
## 5 2013-03-24 Tuggeranong 19 5.4 85 NA
## 6 2019-10-27 Adelaide 15 11.6 58 NA
....
References
Kaiser, Sebastian, Rodrigo Santamaria, Tatsiana Khamiakova, Martin Sill, Roberto Theron, Luis Quintales, Friedrich Leisch, Ewoud De Troyer, and Sami Leon. 2021. Biclust: BiCluster Algorithms. https://CRAN.R-project.org/package=biclust.
Williams, Graham J. 2017. The Essentials of Data Science: Knowledge Discovery Using r. The r Series. CRC Press.
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