eclat_pattern_miner — Logtalk 3.100.1

# `eclat_pattern_miner`

Eclat frequent itemset miner for transaction datasets. Normalizes dataset transaction identifiers to internal ascending ordinals, builds vertical tidsets for frequent singleton items, and recursively extends them by lexicographic suffix joins and tidset intersections.

This library depends on the `frequent_pattern_mining_protocols` support library, implements the generic `pattern_miner_protocol` defined in the `pattern_mining_protocols` core library.

Requires a dataset implementing `transaction_dataset_protocol` with transactions represented as canonical sorted lists of unique declared items. External transaction identifiers are treated as opaque metadata and normalized to internal ordinals before tidset construction.

## API documentation

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

## Loading

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

    | ?- logtalk_load(eclat_pattern_miner(loader)).

## Testing

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

    | ?- logtalk_load(eclat_pattern_miner(tester)).

## Features

- Vertical Tidsets: Represents each frequent extension by the sorted list of transaction identifiers containing it.

- Transaction Ordinal Normalization: Treats external transaction identifiers as opaque metadata and normalizes them to internal ascending ordinals before building vertical tidsets.

- Depth-First Mining: Extends frequent prefixes recursively using tidset intersections.

- 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. When both are given, the absolute-count threshold takes precedence.

- 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:

    eclat_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. Zaki, M. J. (2000) - “Scalable algorithms for association mining”.