dbscan_clusterer — Logtalk 3.100.1

# `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 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.