This function performs clustering analysis using various methods, ordered from simple to comprehensive: k-means (simplest), hierarchical (intermediate), and UMAP+DBSCAN (most comprehensive).
Usage
cluster_embeddings(
data_matrix,
method = "kmeans",
n_clusters = 0,
umap_neighbors = 15,
umap_min_dist = 0.1,
umap_n_components = 10,
umap_metric = "cosine",
dbscan_eps = 0,
dbscan_min_samples = 5,
seed = 123,
verbose = TRUE
)Arguments
- data_matrix
A numeric matrix where rows represent documents and columns represent features.
- method
The clustering method. Options: "kmeans", "hierarchical", "umap_dbscan".
- n_clusters
The number of clusters (for k-means and hierarchical). If 0, optimal number is determined automatically.
- umap_neighbors
The number of neighbors for UMAP (default: 15).
- umap_min_dist
The minimum distance for UMAP (default: 0.1).
- umap_n_components
The number of UMAP components (default: 10).
- umap_metric
The metric for UMAP (default: "cosine").
- dbscan_eps
The eps parameter for DBSCAN. If 0, optimal value is determined automatically.
- dbscan_min_samples
The minimum samples for DBSCAN (default: 5).
- seed
Random seed for reproducibility (default: 123).
- verbose
Logical, if TRUE, prints progress messages.
Examples
if (interactive()) {
mydata <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_cols(
mydata,
listed_vars = c("title", "keyword", "abstract")
)
tokens <- TextAnalysisR::prep_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
data_matrix <- as.matrix(dfm_object)
kmeans_result <- TextAnalysisR::cluster_embeddings(
data_matrix,
method = "kmeans",
n_clusters = 5
)
print(kmeans_result)
hierarchical_result <- TextAnalysisR::cluster_embeddings(
data_matrix,
method = "hierarchical",
n_clusters = 5
)
print(hierarchical_result)
umap_dbscan_result <- TextAnalysisR::cluster_embeddings(
data_matrix,
method = "umap_dbscan",
umap_neighbors = 15,
umap_min_dist = 0.1
)
print(umap_dbscan_result)
}