arXiv:2606.09666v1 Announce Type: new Abstract: Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on representative patterns drawn according to a chosen interestingness measure. In this paper, we address the problem of sampling interval patterns under user-defined syntactic constraints. We introduce CFips, a sampling approach that incorporates constraints directly into the sampling procedure. The approach relies on a multi-step sampling framework and supports several syntactic constraints by dec
The increasing scale and complexity of data necessitate more efficient and targeted methods for knowledge extraction, making advanced sampling techniques crucial.
This development allows for more effective exploration of large pattern spaces, enabling users to focus on representative and relevant patterns rather than exhaustive, computationally expensive searches.
The ability to incorporate user-defined syntactic constraints directly into sampling procedures for interval patterns will significantly improve the utility and efficiency of data mining for specific applications.
- · AI/ML researchers
- · Data scientists
- · Analytics platforms
- · Industries with complex data (e.g., finance, bioinformatics)
- · General-purpose, unoptimized pattern mining solutions
Improved efficiency and relevance of discovered patterns in complex datasets.
Faster development and deployment of AI systems reliant on interpretable pattern discovery.
New applications and insights emerging from previously intractable pattern analysis problems.
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Read at arXiv cs.AI