Basic usage of the package.
library(dplyr) library(ggplot2) library(tglkmeans) theme_set(theme_classic()) set.seed(60427)
First, let's create 5 clusters normally distributed around 1 to 5, with sd of 0.3:
data <- simulate_data(n = 100, sd = 0.3, nclust = 5, dims = 2) data
This is how our data looks like:
data %>% ggplot(aes(x = V1, y = V2, color = factor(true_clust))) + geom_point() + scale_color_discrete(name = "true cluster")
Now we can cluster it using kmeans++:
rownames(data) <- data$id data_for_clust <- data %>% select(starts_with("V")) km <- TGL_kmeans_tidy(data_for_clust, k = 5, metric = "euclid", verbose = TRUE )
The returned list contains 3 fields:
names(km)
km$centers
contains a tibble with clust
column and the cluster centers:
km$centers
clusters are numbered according to order_func
(see 'Custom cluster ordering' section).
km$cluster
contains tibble with id
column with the observation id (1:n
if no id column was supplied), and clust
column with the observation assigned cluster:
km$cluster
km$size
contains tibble with clust
column and n
column with the number of points in each cluster:
km$size
We can now check our clustering performance - fraction of observations that were classified correctly (Note that match_clusters
function is internal to the package and is used only in this vignette):
d <- tglkmeans:::match_clusters(data, km, 5) sum(d$true_clust == d$new_clust, na.rm = TRUE) / sum(!is.na(d$new_clust))
And plot the results:
d %>% ggplot(aes(x = V1, y = V2, color = factor(new_clust), shape = factor(true_clust))) + geom_point() + scale_color_discrete(name = "cluster") + scale_shape_discrete(name = "true cluster") + geom_point(data = km$centers, size = 7, color = "black", shape = "X")
By default, the clusters where ordered using the following function: hclust(dist(cor(t(centers))))
- hclust of the euclidean distance of the correlation matrix of the centers.
We can supply our own function to order the clusters using reorder_func
argument. The function would be applied to each center and he clusters would be ordered by the result.
km <- TGL_kmeans_tidy(data %>% select(id, starts_with("V")), k = 5, metric = "euclid", verbose = FALSE, reorder_func = median ) km$centers
tglkmeans can deal with missing data, as long as at least one dimension is not missing. for example:
data$V1[sample(1:nrow(data), round(nrow(data) * 0.2))] <- NA data
km <- TGL_kmeans_tidy(data %>% select(id, starts_with("V")), k = 5, metric = "euclid", verbose = FALSE ) d <- tglkmeans:::match_clusters(data, km, 5) sum(d$true_clust == d$new_clust, na.rm = TRUE) / sum(!is.na(d$new_clust))
and plotting the results (without the NA's) we get:
d %>% ggplot(aes(x = V1, y = V2, color = factor(new_clust), shape = factor(true_clust))) + geom_point() + scale_color_discrete(name = "cluster") + scale_shape_discrete(name = "true cluster") + geom_point(data = km$centers, size = 7, color = "black", shape = "X")
Let's move to higher dimensions (and higher noise):
data <- simulate_data(n = 100, sd = 0.3, nclust = 30, dims = 300) km <- TGL_kmeans_tidy(data %>% select(id, starts_with("V")), k = 30, metric = "euclid", verbose = FALSE, id_column = TRUE )
Note that here we supplied id_column = TRUE
to indicate that the first column is the id column.
d <- tglkmeans:::match_clusters(data, km, 30) sum(d$true_clust == d$new_clust, na.rm = TRUE) / sum(!is.na(d$new_clust))
Let's compare it to R vanilla kmeans:
km_standard <- kmeans(data %>% select(starts_with("V")), 30) km_standard$clust <- tibble(id = 1:nrow(data), clust = km_standard$cluster) d <- tglkmeans:::match_clusters(data, km_standard, 30) sum(d$true_clust == d$new_clust, na.rm = TRUE) / sum(!is.na(d$new_clust))
We can see that kmeans++ clusters significantly better than R vanilla kmeans.
we can set the seed for reproducible results:
km1 <- TGL_kmeans_tidy(data %>% select(starts_with("V")), k = 30, metric = "euclid", verbose = FALSE, seed = 60427 ) km2 <- TGL_kmeans_tidy(data %>% select(starts_with("V")), k = 30, metric = "euclid", verbose = FALSE, seed = 60427 ) all(km1$centers[, -1] == km2$centers[, -1])
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