Efficient and interpretable maximal frequent fuzzy pattern mining with multi phase pruning and ternary search

The exponential growth of quantitative data across various domains has intensified the need for efficient pattern mining techniques that can handle numerical uncertainty while maintaining interpretability. Traditional fuzzy frequent pattern mining algorithms suffer from pattern explosion in dense datasets, generating overwhelming numbers of redundant patterns that hinder practical analysis. This study introduces a novel Maximal Frequent Fuzzy Pattern Mining (MFPM) framework that integrates fuzzy set theory with maximal pattern representation to address these limitations. The proposed methodology employs a multi-phase approach that incorporates aggressive pruning strategies, including maximum cardinality selection and early termination, to reduce the dimensionality of the search space. Evaluation on three datasets (Chess, Connect and Mushroom) demonstrates consistent gains in both effectiveness and efficiency. Time-wise, MFPM accelerates discovery where classical algorithms are slowest: dense regimes and permissive supports. A ternary search algorithm efficiently identifies the longest patterns, while an Anti-Apriori strategy with superset pruning ensures the extraction of only non-redundant maximal patterns. Experimental evaluation on benchmark datasets demonstrates remarkable effectiveness, achieving up to 94.97% pattern reduction compared to traditional FTDA algorithms while maintaining equivalent knowledge representation. Computational efficiency improved by over 65% in challenging low-support scenarios. The framework generates concise, semantically interpretable patterns that capture the most significant relationships in quantitative data, facilitating informed decision-making across diverse application domains, including healthcare analytics, business intelligence, and web usage mining.