sequential_pattern_mining_protocols

This library provides support entities for sequential pattern mining algorithms. Ordered sequence datasets are represented as objects implementing the sequence_dataset_protocol protocol. The generic pattern_miner_protocol protocol and the pattern_miner_common category used by concrete miners are loaded from the pattern_mining_protocols core library.

The sequential_pattern_mining_common category builds on that generic core with sequential-specific helpers for dataset validation, support count accumulation, and sequential pattern ordering/filtering.

This library also provides reusable sequence smoke-test datasets and a small smoke-test suite.

API documentation

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

Loading

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

| ?- logtalk_load(sequential_pattern_mining_protocols(loader)).

Testing

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

| ?- logtalk_load(sequential_pattern_mining_protocols(tester)).

Test datasets

The test_datasets directory includes the following sample sequence datasets:

• clickstream_sequences.lgt: A compact sequence dataset with repeated prefixes intended for sequential-pattern smoke tests.

• prefix_ladder_sequences.lgt: A small ladder-shaped dataset of singleton events intended for exact baseline checks across sequential mining algorithms.

• same_event_vs_next_event_sequences.lgt: A compact dataset intended to distinguish same-event extensions from next-event extensions.

• repeated_embedding_sequences.lgt: A dataset where the same subsequence admits multiple embeddings inside a single sequence, intended for support-count semantics checks.

• border_threshold_sequences.lgt: A compact dataset with patterns just above and below typical support thresholds, intended for pruning and threshold regression tests.

• closure_sequences.lgt: A compact dataset intended for closed-pattern tests where some frequent patterns share the same support as one of their supersequences.

• dense_overlap_sequences.lgt: A denser dataset with overlapping subsequences and mixed singleton and multi-item events, intended for overlap-heavy mining scenarios.

• branching_sequences.lgt: A dataset with a common prefix and several competing branches, intended for candidate-generation and branching coverage.

The directory also includes invalid fixtures useful for validation and error-handling tests:

• invalid_undeclared_item_sequences.lgt: Uses an item not listed in the declared item domain.

• invalid_unsorted_itemset_sequences.lgt: Uses an event with items not in canonical sorted order.

• invalid_duplicate_item_in_event_sequences.lgt: Uses an event with a duplicate item.

• invalid_empty_event_sequences.lgt: Uses an empty event inside a sequence.