Function reference • tidyclust

Specifications

These cluster specification functions are used to specify the type of model you want to do. These functions work in a similar fashion to the model specification function from parsnip.

k_means(): K-Means

hier_clust(): Hierarchical (Agglomerative) Clustering

cluster_spec: Model Specification Information

cluster_fit: Model Fit Object Information

Fit and Inspect

These functions are the generics that are supported for specifications created with tidyclust.

fit(<cluster_spec>) fit_xy(<cluster_spec>): Fit a Model Specification to a Data Set

set_args(<cluster_spec>): Change arguments of a cluster specification

set_engine(<cluster_spec>): Change engine of a cluster specification

set_mode(<cluster_spec>): Change mode of a cluster specification

augment(<cluster_fit>): Augment data with predictions

glance(<cluster_fit>): Construct a single row summary "glance" of a model, fit, or other object

tidy(<cluster_fit>): Turn a tidyclust model object into a tidy tibble

extract_fit_engine(<cluster_fit>) extract_parameter_set_dials(<cluster_spec>): Extract elements of a tidyclust model object

Prediction

Once the cluster specification have been fit, you are likely to want to look at where the clusters are and which observations are associated with which cluster.

predict(<cluster_fit>) predict_raw(<cluster_fit>): Model predictions

extract_cluster_assignment(): Extract cluster assignments from model

extract_centroids(): Extract clusters from model

Model based performance metrics

These metrics use the fitted clustering model to extract values denoting how well the model works.

cluster_metric_set(): Combine metric functions

silhouette_avg() silhouette_avg_vec(): Measures average silhouette across all observations

sse_ratio() sse_ratio_vec(): Compute the ratio of the WSS to the total SSE

sse_total() sse_total_vec(): Compute the total sum of squares

sse_within_total() sse_within_total_vec(): Compute the sum of within-cluster SSE

silhouette(): Measures silhouette between clusters

sse_within(): Calculates Sum of Squared Error in each cluster

Tuning

Functions to allow multiple cluster specifications to be fit at once.

control_cluster(): Control the fit function

update(<hier_clust>) update(<k_means>): Update a cluster specification

finalize_model_tidyclust() finalize_workflow_tidyclust(): Splice final parameters into objects

tune_cluster(): Model tuning via grid search

Tuning Objects

Dials objects.

cut_height(): Cut Height

linkage_method() values_linkage_method: The agglomeration Linkage method

Developer tools

extract_fit_summary(): S3 method to get fitted model summary info depending on engine

get_centroid_dists(): Computes distance from observations to centroids

new_cluster_metric(): Construct a new clustering metric function

prep_data_dist(): Prepares data and distance matrices for metric calculation

reconcile_clusterings_mapping(): Relabels clusters to match another cluster assignment

translate_tidyclust(): Resolve a Model Specification for a Computational Engine

min_grid(<cluster_spec>): Determine the minimum set of model fits