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Pack logtalk -- logtalk-3.100.1/docs/handbook/_sources/libraries/dbscan_clusterer.rst.txt

.. _library_dbscan_clusterer:

dbscan_clusterer

DBSCAN clusterer. Uses deterministic density-based clustering based on epsilon neighborhoods and minimum point counts. 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#dbscan_clusterer <../../apis/library_index.html#dbscan_clusterer>`__ link in a web browser.

Loading

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

::

| ?- logtalk_load(dbscan_clusterer(loader)).

Testing

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

::

| ?- logtalk_load(dbscan_clusterer(tester)).

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

::

| ?- logtalk_load(dbscan_clusterer(tester_performance)).

Features

  • Density-Based Clustering: Learns density-connected clusters using epsilon neighborhoods and minimum point counts.
  • Adaptive Neighborhood Indexing: Builds a low-dimensional epsilon-grid index when it is likely to be cheaper and otherwise falls back to a deterministic metric tree. The metric-tree path uses adaptive leaf sizing, balanced tie-aware subtree splits, selectable exact or heuristic pivot scoring, and lower-allocation range-query traversal.
  • 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.
  • Reachable-Core Prediction: New instances are assigned to the cluster of the nearest reachable core point within the learned epsilon radius; otherwise the atom noise is returned.
  • 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:

  • epsilon(Epsilon): Neighborhood radius used to determine density connectivity. Default is 1.0.
  • minimum_points(MinimumPoints): Minimum number of points in an epsilon neighborhood for 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.
  • pivot_scoring(PivotScoring): Metric-tree pivot scoring strategy. Options: heuristic (default, single-pass dispersion scoring with one final sort) or exact (more expensive gap-and-range scoring that sorts each candidate profile).

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:

::

dbscan_clusterer(Encoders, Clusters, Noise, Options)

Where:

  • Encoders: List of continuous attribute encoders storing attribute name, mean, and scale.
  • Clusters: List of cluster(Id, CorePoints, BorderPoints) terms in cluster-id order.
  • Noise: List of encoded training points classified as noise.
  • Options: Effective training options used to learn the clusterer.

References

  1. Ester, Kriegel, Sander, and Xu (1996) - "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise". KDD, 226-231.