kmodes_clusterer

k-Modes clusterer. It uses an iterative mode-update algorithm with deterministic initialization and deterministic cluster assignments. Supports discrete attributes only.

The library implements the clusterer_protocol defined in the clustering_protocols library. It provides predicates for learning a clusterer from a dataset, assigning new instances to clusters, and exporting the learned clusterer as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the clustering_dataset_protocol protocol from the clustering_protocols library.

API documentation

Open the ../../apis/library_index.html#kmodes_clusterer link in a web browser.

Loading

To load this library, load the loader.lgt file:

| ?- logtalk_load(kmodes_clusterer(loader)).

Testing

To test this library predicates, load the tester.lgt file:

| ?- logtalk_load(kmodes_clusterer(tester)).

To run the performance benchmark suite, load the tester_performance.lgt file:

| ?- logtalk_load(kmodes_clusterer(tester_performance)).

Features

  • Discrete Datasets: Accepts datasets containing only discrete attributes.

  • Deterministic Initialization: Supports first_k and deterministic spread initialization that repeatedly chooses the farthest example from the modes selected so far.

  • Rich Training Diagnostics: Learned clusterers report training example count, convergence status, iteration count, and final mode shift.

  • Portable Export: Learned clusterers can be exported as clauses or files and reused later.

  • Stable Empty-Cluster Handling: Empty clusters keep their previous modes instead of failing.

Options

The following options can be passed to the learn/3 predicate:

  • k(K): Number of clusters to learn. Default is 2.

  • maximum_iterations(Iterations): Maximum number of mode-update iterations. Default is 100.

  • tolerance(Tolerance): Maximum mode shift threshold for convergence. Default is 0.0.

  • initialization(Initialization): Mode initialization strategy. Options: spread (default) or first_k.

Diagnostics

The diagnostics/2 predicate returns a list containing:

  • model(kmodes_clusterer)

  • mode_count(Count)

  • training_example_count(Count)

  • convergence(Reason)

  • iterations(Count)

  • final_shift(Shift)

  • options(Options)

Clusterer representation

The learned clusterer is represented as a compound term with the functor chosen by the user when exporting the clusterer and arity 4. For example:

kmodes_clusterer(Encoders, Modes, Options, Diagnostics)

Where:

  • Encoders: List of discrete attribute encoders.

  • Modes: List of learned categorical modes in cluster-id order.

  • Options: Effective training options used to learn the clusterer.

  • Diagnostics: Training diagnostics metadata returned by the diagnostics/2 predicate.

References

  1. Huang (1998) - “Extensions to the k-means algorithm for clustering large data sets with categorical values”. Data Mining and Knowledge Discovery, 2, 283-304.