New Sampling Method for Interval Pattern Mining with User-Defined Constraints
Researchers have developed CFips, a sampling approach that enables efficient exploration of interval patterns while respecting user-defined syntactic constraints. The method decomposes constraints into elementary predicates on interval bounds and maintains exact sampling guarantees proportional to pattern frequency. This approach allows pattern mining tasks to complete within time limits that would otherwise cause exhaustive methods to fail.
A new paper on arXiv presents CFips, a frequency-based constrained sampling framework designed to address the computational challenges of interval pattern mining. Rather than exhaustively exploring large pattern spaces, the method enables users to sample representative patterns according to chosen interestingness measures while adhering to syntactic constraints. The approach uses a multi-step sampling framework that decomposes constraints into elementary predicates on interval bounds, formally preserving exact sampling guarantees where patterns are selected proportionally to their frequency within the constrained space. Experimental results demonstrate that integrating constraints directly into the sampling procedure enables mining tasks to complete successfully within specified time limits, addressing a key limitation of traditional exhaustive pattern mining approaches.
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- arXiv cs.AICenter
Frequency-based Constrained Sampling for Interval Patterns
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