fp_growth_pattern_miner — Logtalk 3.100.1

# `fp_growth_pattern_miner`

FP-growth frequent itemset miner for transaction datasets. The library depends on the `frequent_pattern_mining_protocols` support library, implements the generic `pattern_miner_protocol` defined in the `pattern_mining_protocols` core library, and mines frequent itemsets using recursive conditional pattern-base projection over a compact FP-tree whose nodes store parent links directly, plus header-table node chains derived from the final tree for direct conditional-base reconstruction, without candidate generation.

Requires a dataset implementing `transaction_dataset_protocol` with transactions represented as canonical sorted lists of unique declared items.

## API documentation

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

## Loading

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

    | ?- logtalk_load(fp_growth_pattern_miner(loader)).

## Testing

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

    | ?- logtalk_load(fp_growth_pattern_miner(tester)).

## Features

- FP-tree Construction: Builds a compact prefix tree from frequent items ordered by global support.

- Header and Parent Links: Stores parent links directly in tree nodes and derives header-table node chains from the final tree so conditional pattern bases are reconstructed by walking parent links instead of using a separate node index.

- Pattern Growth: Mines frequent itemsets recursively from conditional pattern bases without candidate generation.

- Canonical Transactions: Validates that transactions are sorted, duplicate-free, 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 itemset length to mine. The default is `1000`, which is effectively capped by the longest transaction in the dataset.

- `minimum_pattern_length/1`: Minimum itemset length 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:

    fp_growth_pattern_miner(ItemDomain, Patterns, Options)

Where:

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

- `Patterns`: List of `itemset(Items, SupportCount)` terms ordered first by pattern length and then lexicographically.

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

## References

1. Han, J., Pei, J., and Yin, Y. (2000) - “Mining frequent patterns without candidate generation”.