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.. _library_optics_clusterer:

optics_clusterer

OPTICS clusterer. It uses deterministic OPTICS ordering with epsilon-based cluster extraction for the fixed clusterer protocol. Supports continuous 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#optics_clusterer <../../apis/library_index.html#optics_clusterer>`__ link in a web browser.

Loading

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

::

| ?- logtalk_load(optics_clusterer(loader)).

Testing

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

::

| ?- logtalk_load(optics_clusterer(tester)).

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

::

| ?- logtalk_load(optics_clusterer(tester_performance)).

Features

  • OPTICS Ordering: Learns a deterministic ordering using density-based reachability over continuous datasets.
  • Adaptive Neighborhood Indexing: Uses a low-dimensional epsilon-grid index when it is likely to be cheaper and otherwise falls back to a deterministic metric tree for neighborhood search during ordering construction. The search backend can also be selected explicitly.
  • Continuous Datasets: Accepts datasets containing only continuous attributes.
  • Distance Metrics: Supports Euclidean and Manhattan distances.
  • Optional Feature Scaling: Continuous attributes can be standardized using z-score scaling.
  • Epsilon-Based Extraction: Extracts clusters from the ordering using a configurable extraction epsilon threshold.
  • Noise Detection: New instances not reachable from an extracted core cluster within the extraction threshold are assigned to noise.
  • Prediction Pruning: Classification reuses per-cluster core-point bounds to prune clusters that cannot beat the current best reachable match.
  • Portable Export: Learned clusterers can be exported as clauses or files and reused later.

Options

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

  • ordering_and_extraction_epsilons(MaximumOrderingEpsilon, ExtractionEpsilon): Pair of epsilon thresholds where MaximumOrderingEpsilon is the neighborhood radius used while constructing the OPTICS ordering and ExtractionEpsilon is the threshold used when extracting clusters from the learned ordering and when classifying new instances. Default is ordering_and_extraction_epsilons(1.0, 1.0). ExtractionEpsilon must not be greater than MaximumOrderingEpsilon.
  • search_index(SearchIndex): Search backend selection used while constructing the OPTICS ordering. Options are auto (default), grid, and metric_tree.
  • minimum_points(MinimumPoints): Minimum neighborhood size required for a point to be considered a core point. Default is 2.
  • distance_metric(Metric): Distance metric to use. Options: euclidean (default) or manhattan.
  • feature_scaling(FeatureScaling): Whether to standardize continuous attributes before clustering. Options: on (default) or off.

Clusterer representation

The learned clusterer is represented as a compound term with the functor chosen by the user when exporting the clusterer and arity 5. For example:

::

optics_clusterer(Encoders, Ordering, Clusters, Noise, Options)

Where:

  • Encoders: List of continuous attribute encoders storing attribute name, mean, and scale.
  • Ordering: List of ordered points annotated with reachability and core-distance information.
  • Clusters: List of extracted clusters in cluster-id order.
  • Noise: List of extracted noise points.
  • Options: Effective training options used to learn the clusterer.