How we implemented frequent item set mining in Elasticsearch
Blog post from Elastic
Elasticsearch has introduced the frequent_item_sets aggregation, a technique for frequent item set mining, to identify patterns in large datasets, marking its general availability with version 8.7. This feature leverages the Eclat algorithm, chosen over the Apriori for its efficient use of resources through a depth-first approach, to scale better in runtime and memory. The process involves mapping and reducing phases where items and transactions are de-duplicated, encoded, and pruned based on minimum support parameters, optimizing the discovery of top-N frequent closed item sets. Despite being resource-intensive, the implementation incorporates various optimizations such as circuit breakers, async search, and filtering to mitigate its impact on runtime and resource usage. Frequent_item_sets can be integrated with other Elasticsearch functionalities, allowing users to discover data regularities, and is particularly utilized in AIOps Labs' Explain Log Rate Spikes for log analysis.
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