AMFUP: An Adaptive Multi-Modal Framework for Unexpected Pattern Discovery in Dynamic Environments

Abstract

The discovery of unexpected patterns represents a critical advancement in knowledge discovery, enabling the identification of rare yet meaningful contradictions that defy conventional frequency-based assumptions. However, existing techniques frequently suffer from three major limitations: (1) rigidity in adapting to evolving data distributions, (2) limited capacity to interpret semantic relationships among patterns, and (3) dependency on extensive manual tuning for parameter optimization. To overcome these challenges, this paper introduces the Adaptive Multi-Modal Framework for Unexpected Pattern Discovery (AMFUP), a comprehensive architecture that enhances adaptability, semantic understanding, and automation in pattern mining. AMFUP integrates three synergistic components: the Multi-Modal Pattern Embedding (MMPE), which captures structural, semantic, and statistical dimensions of patterns through neural architectures; the Dynamic Belief Adaptation (DBA) module, which continuously evolves belief systems in response to concept drift; and the Automated Parameter Learning (APL) mechanism, which employs meta-learning to optimize parameters without human intervention. Experimental results across eight datasets demonstrate that AMFUP achieves the highest Pattern Quality Score (PQS) of 0.96, representing a 28% improvement over the best baseline method (belief-driven). Concurrently, AMFUP establishes itself as the fastest method with a runtime of 12 minutes, achieving a 73.3% reduction compared to UCRP-miner (45 min) and a 33.3% reduction compared to Random Forest (18 min). AMFUP achieves a 43.28% increase in PQS and a 46.68% reduction in execution runtime compared to the average performance of baseline methods.