pattern_mining_protocols — Logtalk 3.100.1

# `pattern_mining_protocols`

This library provides the generic core entities used in the implementation of machine learning pattern-finding algorithms. Pattern-mining algorithm implementations typically import the `pattern_miner_common` category, which implements the `pattern_miner_protocol` protocol and provides shared defaults, option handling, diagnostics accessors, and export helpers.

The family-specific dataset protocols and bundled smoke-test datasets are provided by the `frequent_pattern_mining_protocols` and `sequential_pattern_mining_protocols` support libraries.

## API documentation

Open the ../../apis/library_index.html#pattern_mining_protocols link in a web browser.

## Loading

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

    | ?- logtalk_load(pattern_mining_protocols(loader)).

## Testing

To run the library smoke tests, load the `tester.lgt` file:

    | ?- logtalk_load(pattern_mining_protocols(tester)).

## Common options

The `pattern_miner_common` category supports the following options used by importing pattern miners to control support thresholds and pattern length filtering:

- `minimum_support(0.5)` sets the relative minimum support threshold as a proportion in the interval `]0.0, 1.0]`.

- `minimum_support_count(N)` sets the absolute minimum support count. If both support options are provided, this option takes precedence.

- `maximum_pattern_length(1000)` sets the maximum pattern length to mine. The effective value is capped by the longest transaction or sequence in the dataset.

- `minimum_pattern_length(1)` sets the minimum pattern length retained in the mined result.

The current `apriori_pattern_miner`, `eclat_pattern_miner`, `fp_growth_pattern_miner`, and `prefix_span_pattern_miner` libraries all use these shared defaults.

## Diagnostics

The `pattern_miner_protocol` protocol also defines the `diagnostics/2`, `diagnostic/2`, and `pattern_miner_options/2` predicates. These expose representation-independent metadata about mined results.

All pattern miners now provide at least the following generic diagnostics terms:

- `model(Model)`

- `options(Options)`

- `item_domain_size(Size)`

- `pattern_count(Count)`

- `pattern_length_histogram(Histogram)`

- `support_range(MinimumSupport, MaximumSupport)`

Each miner also provides algorithm-specific diagnostics terms describing its mining strategy and support representation, such as candidate-generation style, projected-database growth, closure filtering, or vertical support layout.

## Related support libraries

- `frequent_pattern_mining_protocols`: Provides the `transaction_dataset_protocol` protocol plus bundled transaction smoke-test datasets for frequent itemset miners.

- `sequential_pattern_mining_protocols`: Provides the `sequence_dataset_protocol` protocol plus bundled sequence smoke-test datasets for sequential pattern miners.