# `spade_pattern_miner`

SPADE sequential pattern miner for sequence datasets. The library depends on the `sequential_pattern_mining_protocols` support library, implements the generic `pattern_miner_protocol` defined in the `pattern_mining_protocols` core library, and mines frequent sequential patterns using Zaki’s equivalence-class decomposition with temporal joins over vertical occurrence lists keyed by sequence and event identifiers.

Requires a dataset implementing `sequence_dataset_protocol` with sequences represented as ordered lists of canonical sorted itemsets over a declared item domain.

## API documentation

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

## Loading

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

    | ?- logtalk_load(spade_pattern_miner(loader)).

## Testing

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

    | ?- logtalk_load(spade_pattern_miner(tester)).

## Features

- Equivalence-Class Decomposition: Mines frequent sequences using Zaki’s equivalence-class decomposition over prefix-sharing classes.

- Vertical Occurrence Lists: Represents frequent patterns using sequence and event occurrence lists.

- Active Class Candidate Pruning: Restricts class joins to members that occur in the supporting sequences of the current prefix, using per-sequence member-id indexes instead of storing full pattern terms in the sequence index.

- Same-Event and Sequence Extensions: Supports both itemset growth and next-event sequence growth.

- Canonical Sequences: Validates that itemsets are sorted, duplicate-free, non-empty, and restricted to declared items.

- Flexible Support Thresholds: Supports relative minimum support and absolute minimum support count.

- Model Export: Mined pattern collections can be exported as predicate clauses or written to a file.

## Options

The `mine/3` predicate accepts the following options:

- `minimum_support/1`: Relative minimum support threshold in the interval `]0.0, 1.0]`. The default is `0.5`.

- `minimum_support_count/1`: Absolute minimum support count. When both support options are provided, this option takes precedence.

- `maximum_pattern_length/1`: Maximum total number of items in a mined sequential pattern. The default is `1000`, effectively capped by the longest sequence in the dataset.

- `minimum_pattern_length/1`: Minimum total number of items retained in the mined result. The default is `1`.

## Pattern miner representation

The mined pattern miner result is represented by a compound term with the functor chosen by the implementation and arity 3. For example:

    spade_pattern_miner(ItemDomain, Patterns, Options)

Where:

- `ItemDomain`: Canonical sorted list of declared dataset items.

- `Patterns`: List of `sequence_pattern(Pattern, SupportCount)` terms ordered first by total item count and then lexicographically.

- `Options`: Effective mining options used to mine the frequent sequential patterns.

## References

1. Zaki, M. J. (2001) - “SPADE: An efficient algorithm for mining frequent sequences”.