Explainable Fuzzy Utility Mining on Sequences

Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability interpretability. To obtain a model for decision making, this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. Meanwhile, more normative formulation of the problem fuzzy sequences is formulated. By exploring set theory mining, propose novel method termed pattern growth (PGFUM) high-utility linguistic meaning. In case sequence data, PGFUM reflects quantity regions sequences. improve efficiency feasibility PGFUM, develop two compressed structures fuzziness. Furthermore, one existing new upper bounds candidates are adopted three proposed pruning strategies to substantially reduce search space and, thus, expedite process. It demonstrated that achieves not only results contain original nature revealable intelligibility, but also high terms runtime memory cost.