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

Abstract

The discovery of unexpected patterns is pivotal in enhancing knowledge discovery beyond conventional frequency-oriented methodologies. Current techniques, such as UCRP-miner and those based on clustering, are suffer from three major limitations: dependence on static belief systems, susceptibility to manual parameter optimization, and a failure to effectively capture semantic relationships among patterns. This paper presents AMFUP (Adaptive Multi-Modal Framework for Unexpected Pattern Discovery), an innovative framework designed to overcome these deficiencies through a unified integration of three components. Initially, the Multi-Modal Pattern Embedding (MMPE) module utilizes neural architectures to semantically, structurally, and statistically represent patterns, thereby facilitating precise similarity evaluations. Secondly, the Dynamic Belief Adaptation (DBA) module permits the evolution of beliefs in response to data fluctuations, ensuring both theoretical integrity and adaptability. Lastly, the Automated Parameter Learning (APL) component employs meta-learning techniques to enhance the optimization of parameters across multiple datasets without necessitating manual input. The suggested framework was subjected to assessment across eight separate datasets, illustrating an improvement in performance compared to five baseline approaches, with a 40% enhancement in pattern quality and a 70% reduction in runtime. Notably, AMFUP exhibited strong cross-domain performance, achieving a transfer accuracy of 78%, thereby highlighting its remarkable generalizability. The innovations introduced by AMFUP in embedding, adaptivity, and automation represent substantial progress in the domain of unexpected pattern mining. By addressing enduring challenges related to scalability, generalization, and semantic comprehension, this framework facilitates the development of robust applications within dynamic sectors such as healthcare, finance, and cybersecurity. The study provides not only advancements in algorithms but also actionable insights that have a meaningful impact on society.