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SPDX-FileCopyrightText: 1998-2026 Paulo Moura <pmoura@logtalk.org>
SPDX-License-Identifier: Apache-2.0

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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    http://www.apache.org/licenses/LICENSE-2.0

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`hdbscan_clusterer`
=========

Simplified HDBSCAN-style clusterer. It builds the mutual-reachability
graph, computes a minimum spanning tree, derives the single-linkage
hierarchy, condenses the hierarchy using `minimum_cluster_size`, and
selects clusters using `eom` or `leaf` selection. 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#hdbscan_clusterer](../../apis/library_index.html#hdbscan_clusterer)
link in a web browser.


Loading
-------

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

	| ?- logtalk_load(hdbscan_clusterer(loader)).


Testing
-------

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

	| ?- logtalk_load(hdbscan_clusterer(tester)).

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

	| ?- logtalk_load(hdbscan_clusterer(tester_performance)).


Features
--------

- **Hierarchical Density Clustering**: Builds the mutual-reachability graph, computes a minimum spanning tree, derives the single-linkage hierarchy, condenses it using `minimum_cluster_size`, and selects clusters using `eom` or `leaf` selection.
- **Continuous Datasets**: Accepts datasets containing only continuous attributes.
- **Cluster Selection Methods**: Supports both `eom` and `leaf` cluster selection.
- **Distance Metrics**: Supports Euclidean and Manhattan distances.
- **Optional Feature Scaling**: Continuous attributes can be standardized using z-score scaling.
- **Reachability-Based Prediction**: New instances are assigned to the selected cluster with the nearest training point when the distance is within the learned cluster reachability threshold; otherwise the atom `noise` is returned.
- **Noise Detection**: Points not assigned to any extracted cluster are retained as noise.
- **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:

- `minimum_points(MinimumPoints)`: Minimum neighborhood size used when computing core distances and mutual reachability. Default is `2`.
- `minimum_cluster_size(MinimumClusterSize)`: Minimum number of points required for an extracted cluster. Default is `2`.
- `cluster_selection_method(Method)`: Cluster extraction policy. Options: `eom` (default) or `leaf`.
- `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 4.
For example:

	hdbscan_clusterer(Encoders, Clusters, Noise, Options)

Where:

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