define_clusters {evprof}

Define each cluster with a user profile interpretation

Description

Every cluster has a centroid (i.e. average start time and duration) that can be related to a daily human behaviour or connection pattern (e.g. Worktime, Dinner, etc.). In this function, a user profile name is assigned to every cluster.

Usage

define_clusters(
  models,
  interpretations = NULL,
  profile_names = NULL,
  log = FALSE
)

Arguments

models

tibble, parameters of the clusters' GMM models obtained with function cluster_sessions() (object models of the returned list)

interpretations

character vector with interpretation sentences of each cluster (arranged by cluster number)

profile_names

character vector with user profile assigned to each cluster (arranged by cluster number)

log

logical, whether to transform ConnectionStartDateTime and ConnectionHours variables to natural logarithmic scale (base = exp(1)).

Value

tibble object

Examples

library(dplyr)

# Select working day sessions (`Timecycle == 1`) that
# disconnect the same day (`Disconnection == 1`)
sessions_day <- california_ev_sessions %>%
  divide_by_timecycle(
    months_cycles = list(1:12), # Not differentiation between months
    wdays_cycles = list(1:5, 6:7) # Differentiation between workdays/weekends
  ) %>%
  divide_by_disconnection(
    division_hour = 10, start = 3
  ) %>%
  filter(
    Disconnection == 1, Timecycle == 1
  ) %>%
  sample_frac(0.05)
plot_points(sessions_day, start = 3)

# Identify two clusters
sessions_clusters <- cluster_sessions(
  sessions_day, k=2, seed = 1234, log = TRUE
)

# Plot the clusters found
plot_bivarGMM(
  sessions = sessions_clusters$sessions,
  models = sessions_clusters$models,
  log = TRUE, start = 3
)

# Define the clusters with user profile interpretations
define_clusters(
  models = sessions_clusters$models,
  interpretations = c(
    "Connections during working hours",
    "Connections during all day (high variability)"
  ),
  profile_names = c("Workers", "Visitors"),
  log = TRUE
)