agglomerative_clusterer — Logtalk 3.100.1

# `agglomerative_clusterer`

Agglomerative clusterer.

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#agglomerative_clusterer link in a web browser.

## Loading

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

    | ?- logtalk_load(agglomerative_clusterer(loader)).

## Testing

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

    | ?- logtalk_load(agglomerative_clusterer(tester)).

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

    | ?- logtalk_load(agglomerative_clusterer(tester_performance)).

## Features

- Bottom-Up Clustering: Uses deterministic bottom-up agglomerative_clusterer clustering and stops when the requested number of clusters is reached.

- Continuous Datasets: Accepts datasets containing only continuous attributes.

- Linkage Strategies: Supports `single`, `complete`, and `average` linkage.

- Distance Metrics: Supports `euclidean` and `manhattan` distances.

- Optional Feature Scaling: Continuous attributes can be standardized using z-score scaling.

- Linkage-Aware Prediction: New instances are assigned to the nearest learned cluster using the selected linkage strategy and distance metric applied to the learned cluster members.

- Deterministic Ordering: Equal-distance merges are broken using node-id order and final clusters are ordered by minimum training example id so equivalent dataset permutations keep the same cluster ids.

- Cached Distances: Inter-cluster distances are cached and incrementally updated after each merge instead of being fully recomputed from cluster members at every iteration.

- Priority-Queue Merge Selection: Candidate merges are tracked in a min-heap keyed by distance and node-id order, allowing stale entries to be discarded lazily while keeping merge selection deterministic.

- Rich Diagnostics: Diagnostics report the training example count, performed merge count, initial pair count, maximum heap size, stale-pair discard count, deterministic pair-selection strategy, and linkage-aware prediction strategy.

- Fail-Fast Consistency Checks: Internal heap, active-node, and cached-distance inconsistencies raise explicit `agglomerative_error/2` exceptions instead of failing silently.

- 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:

- `k(K)`: Number of clusters to retain after merging. Default is `2`.

- `linkage(Linkage)`: Linkage strategy to use. Options: `single`, `complete`, or `average` (default).

- `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:

    agglomerative_clusterer(Encoders, Clusters, Prototypes, Options, Diagnostics)

Where:

- `Encoders`: List of continuous attribute encoders storing attribute name, mean, and scale.

- `Clusters`: List of `cluster(Id, Points)` terms in cluster-id order.

- `Prototypes`: List of average vectors used for display, diagnostics, and export metadata.

- `Options`: Effective training options used to learn the clusterer.

- `Diagnostics`: Training metadata including heap and prediction details.

## Diagnostics

The `diagnostics/2` predicate returns metadata including:

- `training_example_count/1`

- `merge_count/1`

- `initial_pair_count/1`

- `maximum_heap_size/1`

- `stale_pair_discard_count/1`

- `pair_selection(priority_queue)`

- `prediction_strategy(cluster_member_linkage_distance)`

- `tie_breaking(node_id_order)`

- `options/1`