hierarchical_clustering — Logtalk 3.100.1

# `hierarchical_clustering`

Hierarchical clusterer. Supports continuous attributes only. It builds the full bottom-up agglomerative_clusterer hierarchy and derives the requested partition by cutting the learned dendrogram at the largest remaining merge distances.

The library implements the `clusterer_protocol` defined in the `clustering_protocols` library. It provides predicates for learning a full agglomerative_clusterer hierarchy from a dataset, deriving a `k`-cluster cut for prediction, 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#hierarchical_clustering link in a web browser.

## Loading

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

    | ?- logtalk_load(hierarchical_clustering(loader)).

## Testing

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

    | ?- logtalk_load(hierarchical_clustering(tester)).

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

    | ?- logtalk_load(hierarchical_clustering(tester_performance)).

## Features

- Full Hierarchy Learning: Builds the complete bottom-up merge hierarchy before deriving the requested cluster partition.

- Deterministic Dendrogram Cutting: Produces the final `k` clusters by repeatedly splitting the highest remaining merge.

- 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 rather than a prototype shortcut.

- Incremental Distance Updates: Training caches singleton pair distances once and updates merge distances incrementally for `single`, `complete`, and `average` linkage instead of recomputing all member-pair distances after every merge.

- Heap-Based Merge Selection: Training uses a lazy min-heap of candidate cluster pairs and skips stale entries for merged-away nodes instead of rescanning all active pairs at every agglomeration step.

- Reusable Hierarchy Cuts: Learned hierarchies can be re-cut to a different `k` using `cut/3` without retraining.

- Rich Diagnostics: Learned clusterers record merge counts, dendrogram height, heap rebuilds, scan fallbacks, maximum heap size, and effective options.

- Deterministic Tie-Breaking: Equal merge distances and equal split heights are resolved by preferring the smallest node-id pair.

- 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 cutting the learned hierarchy. 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 5. For example:

    hierarchical_clustering_clusterer(Encoders, hierarchy(RootState, MergeRecords, Dendrogram), Clusters, Prototypes, Diagnostics)

Where:

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

- `hierarchy(RootState, MergeRecords, Dendrogram)`: Reusable hierarchy state. `RootState` and `MergeRecords` are used internally by `cut/3`, and `Dendrogram` is the learned merge tree represented with `leaf(Id)` and `merge(Left, Right, Distance, Size)` terms.

- `Clusters`: List of `cluster(Id, Points)` terms for the selected `k`-cluster cut.

- `Prototypes`: List of average vectors kept for display and export metadata.

- `Diagnostics`: Diagnostics metadata including the effective training options used to learn the clusterer.

## Additional API

- `cut(+Clusterer, +K, -RecutClusterer)`: Reuses the learned hierarchy to derive a new `K`-cluster cut without retraining.

## Diagnostics

The `diagnostics/2` predicate returns metadata terms including:

- `model(hierarchical_clustering)`

- `cluster_count(Count)`

- `prototype_count(Count)`

- `training_example_count(Count)`

- `merge_count(Count)`

- `dendrogram_height(Height)`

- `heap_rebuild_count(Count)`

- `scan_fallback_count(Count)`

- `maximum_heap_size(Size)`

- `tie_breaking(node_id_order)`

- `options(Options)`